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main.py
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import tkinter as tk
from tkinter import ttk
import pandas as pd
import customtkinter as ctk
import os
from PIL import Image
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import matplotlib
matplotlib.use('TkAgg')
import numpy as np
import scipy.stats as stats
import seaborn as sns
from scipy.stats import norm
from scipy.stats import zscore
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.svm import SVC
from sklearn.metrics import r2_score
ctk.set_appearance_mode("Light")
class GUI:
def __init__(self, root):
self.root = root
self.root.title("Heart Attack Predictor")
self.root.geometry(f"{1200}x{650}")
self.root.configure(bg_color="white")
self.root.grid_rowconfigure(0,weight=1)
self.root.grid_columnconfigure(0,weight=1)
self.root.grid_columnconfigure(1,weight=1)
self.root.grid_columnconfigure(2,weight=1)
self.sidebar_light="white"
self.sidebar_button_text_light="#201E1F"
self.content_frame1_light="#F4FAFF"
self.home_frames_light="white"
self.home_text_light="#201E1F"
self.sidebar_buttons_hover_light="#F6F4D2"
self.sidebar_dark = "#201E1F"
self.sidebar_button_text_dark = "#E2CFEA"
self.home_frames_dark = "#201E1F"
self.home_text_dark = ""
self.content_frame1_dark = "#E2CFEA"
self.sidebar_buttons_hover_dark=""
# Create a sidebar
self.sidebar = ctk.CTkFrame(root, corner_radius=0,fg_color=(self.sidebar_light,self.sidebar_dark),border_color="red")
self.sidebar.grid(row=0, column=0,rowspan=7, sticky="nsew")
self.sidebar.grid_rowconfigure(2,weight=1)
self.sidebar.grid_columnconfigure(1,weight=1)
self.logo_frame=ctk.CTkFrame(self.sidebar,corner_radius=0,fg_color=(self.sidebar_light,self.sidebar_dark))
self.logo_frame.grid(row=0,column=0,padx=(10,0),pady=(20,0),sticky="nsew")
self.buttons_frame=ctk.CTkFrame(self.sidebar,corner_radius=0,fg_color=(self.sidebar_light,self.sidebar_dark),width=150)
self.buttons_frame.grid(row=2,pady=(30,0),column=0,columnspan=2,sticky="nsew")
self.buttons_frame.grid_columnconfigure(0,weight=1)
# Create buttons in the sidebar
# script_dir = os.path.dirname(os.path.realpath(__file__))
# images_path = os.path.join(script_dir, "test images")
self.logo_image = ctk.CTkImage(Image.open("test images/menu.png"), size=(26, 26))
self.home_image = ctk.CTkImage(Image.open("test images/home.png"), size=(26, 26))
self.chart_image = ctk.CTkImage(Image.open("test images/chart.png"), size=(26, 26))
self.predict_image = ctk.CTkImage(Image.open("test images/predictive.png"), size=(26, 26))
self.logo_label = ctk.CTkLabel(self.logo_frame, text=" Sidebar",text_color=(self.sidebar_button_text_light,self.sidebar_button_text_dark), image=self.logo_image, compound="left", font=ctk.CTkFont(size=20, weight="bold"))
self.logo_label.grid(row=0, column=0, padx=5, pady=10)
self.home_button = ctk.CTkButton(self.buttons_frame, corner_radius=10, height=40, border_spacing=10, text="Home", image=self.home_image,fg_color="transparent", text_color=(self.sidebar_button_text_light,self.sidebar_button_text_dark), hover_color=(self.sidebar_buttons_hover_light,self.sidebar_buttons_hover_dark), anchor="w", command=self.show_home,font=ctk.CTkFont("Poppins",size=16,weight="bold"))
self.home_button.grid(row=2, column=0, sticky="ew")
self.chart_viewer_button = ctk.CTkButton(self.buttons_frame, corner_radius=10, height=40, border_spacing=10, text="Chart Viewer", image=self.chart_image, fg_color="transparent", text_color=(self.sidebar_button_text_light,self.sidebar_button_text_dark), hover_color=(self.sidebar_buttons_hover_light,self.sidebar_buttons_hover_dark), anchor="w", command=self.show_data_viewer,font=ctk.CTkFont("Poppins",size=16,weight="bold"))
self.chart_viewer_button.grid(row=3, column=0, sticky="ew")
self.predict_button = ctk.CTkButton(self.buttons_frame, corner_radius=10, height=40, border_spacing=10, text="Predict", image=self.predict_image,fg_color="transparent", text_color=(self.sidebar_button_text_light,self.sidebar_button_text_dark), hover_color=(self.sidebar_buttons_hover_light,self.sidebar_buttons_hover_dark), anchor="w",command=self.predict_model,font=ctk.CTkFont("Poppins",size=16,weight="bold"))
self.predict_button.grid(row=4, column=0, sticky="ew")
# self.appearance_mode_label = ctk.CTkLabel(self.sidebar, text="Appearance Mode:",text_color=(self.sidebar_button_text_light,self.sidebar_button_text_dark), anchor="w")
# self.appearance_mode_label.grid(row=5, column=0, padx=20, pady=(10, 0))
# self.appearance_mode_optionmenu = ctk.CTkOptionMenu(self.sidebar, values=["Light", "Dark", "System"], command=self.change_appearance_mode_event)
# self.appearance_mode_optionmenu.grid(row=6, column=0, padx=20, pady=(10, 10))
# self.appearance_mode_optionmenu.set("Light")
self.show_home()
def show_home(self):
if 'self.content_frame1' in globals() and self.content_frame1.winfo_exists():
for widget in self.content_frame1.winfo_children():
widget.destroy()
self.content_frame1.destroy()
if 'self.predict_frame' in globals() and self.predict_frame.winfo_children():
for widget in self.predict_tab1.winfo_children():
widget.destroy()
self.predict_frame.destroy()
# Create a new frame for home
self.content_frame = ctk.CTkFrame(self.root, corner_radius=0,
fg_color=(self.content_frame1_light, self.content_frame1_dark))
self.content_frame.grid(row=0, column=1, columnspan=15, rowspan=7, padx=10, sticky="nsew")
# self.content_frame.grid_rowconfigure(4,weight=1)
self.basic_desc_frame=ctk.CTkFrame(self.content_frame,corner_radius=0,fg_color=(self.content_frame1_light,self.content_frame1_dark))
self.basic_desc_frame.grid(row=0,column=0,columnspan=4,rowspan=4,sticky="nsew")
self.basic_desc_frame.grid_rowconfigure((0,1),weight=1)
self.basic_desc_frame.grid_columnconfigure((0,3),weight=1)
#Frame for count
self.count_frame=ctk.CTkFrame(self.basic_desc_frame,corner_radius=10,width=250,height=100,fg_color=(self.home_frames_light,self.home_frames_dark))
self.count_frame.grid(row=0,column=0,padx=(10,0),pady=(10,0),sticky="nsew")
# Frame for Mean
self.mean_frame = ctk.CTkFrame(self.basic_desc_frame,corner_radius=10,fg_color=(self.home_frames_light,self.home_frames_dark))
self.mean_frame.grid(row=0, column=3, padx=(10, 0), pady=(10, 0), sticky="nsew")
self.std_frame=ctk.CTkFrame(self.basic_desc_frame,corner_radius=10,fg_color=(self.home_frames_light,self.home_frames_dark))
self.std_frame.grid(row=2,column=0, padx=(10, 0), pady=(10, 0), sticky="nsew")
self.heart_frame = ctk.CTkFrame(self.basic_desc_frame,corner_radius=10,fg_color=(self.home_frames_light,self.home_frames_dark))
self.heart_frame.grid(row=2, column=3, padx=(10, 0), pady=(10, 0), sticky="nsew")
self.user_frame=ctk.CTkFrame(self.content_frame,corner_radius=10,width=200,fg_color=(self.home_frames_light,self.home_frames_dark))
self.user_frame.grid(row=0,column=4,rowspan=4,padx=(10,0), pady=(10, 0),sticky="nsew")
self.acc_frame=ctk.CTkFrame(self.content_frame,corner_radius=10,width=200,fg_color=(self.home_frames_light,self.home_frames_dark))
self.acc_frame.grid(row=0,column=5,rowspan=4,padx=(10,0),pady=(10, 0),sticky="nsew")
self.pie_tab=ctk.CTkTabview(self.content_frame,width=400,height=200,corner_radius=10,text_color='black',fg_color=(self.home_frames_light,self.home_frames_dark),segmented_button_fg_color="white",segmented_button_selected_hover_color="#F5F1ED",segmented_button_unselected_color="white",segmented_button_selected_color="#F5F1ED")
self.pie_tab.grid(row=4,column=1,columnspan=2,rowspan=3,padx=(10,0),pady=(10,0),sticky="nsew")
self.pie_tab1=self.pie_tab.add('Tab 1')
self.pie_tab2=self.pie_tab.add('Tab 2')
self.pie_tab2.grid_columnconfigure(1,weight=1)
self.chart_tab=ctk.CTkTabview(self.content_frame,width=400,corner_radius=10,text_color='black',fg_color=(self.home_frames_light,self.home_frames_dark),segmented_button_fg_color="white",segmented_button_selected_hover_color="#F5F1ED",segmented_button_unselected_color="white",segmented_button_selected_color="#F5F1ED")
self.chart_tab.grid(row=4,column=4,columnspan=5,rowspan=3,padx=(10,0),pady=(10,0),sticky="nsew")
self.chart_tab1=self.chart_tab.add('Tab 1')
self.chart_tab2=self.chart_tab.add('Tab 2')
self.chart_tab3=self.chart_tab.add('Tab 3')
self.chart_tab2.grid_columnconfigure(2,weight=1)
self.male_label=ctk.CTkLabel(self.count_frame,text="Male",width=50,text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway",size=14))
self.male_label.grid(row=0,column=0,padx=(10,0),pady=(10,0),sticky="nsew")
self.male_value=ctk.CTkLabel(self.count_frame,text=str(male_count)+"\n",width=50,text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway",size=18,weight='bold'))
self.male_value.grid(row=1,column=0,padx=(20,0),sticky="nsew")
self.female_label=ctk.CTkLabel(self.mean_frame,text="Female",width=50,text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway",size=14))
self.female_label.grid(row=0,column=0,padx=(10,0),pady=(10,0),sticky="nsew")
self.female_value=ctk.CTkLabel(self.mean_frame,text=str(female_count),width=50,text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway",size=18,weight='bold'))
self.female_value.grid(row=1,column=0,padx=(20,0),sticky="nsew")
self.chloes_label=ctk.CTkLabel(self.std_frame,text="Cholestrol",width=50,text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway",size=14))
self.chloes_label.grid(row=0,column=0,padx=(10,0),pady=(10,0))
self.chloes_label=ctk.CTkLabel(self.std_frame,text=str(round(oa_chol,1))+"\n",width=50,text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway",size=18,weight='bold'))
self.chloes_label.grid(row=1,column=0,padx=(20,0),sticky="nsew")
self.heart_label=ctk.CTkLabel(self.heart_frame,text="Heart Cases",width=50,text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway",size=14))
self.heart_label.grid(row=0,column=0,padx=(10,0),pady=(10,0),sticky="nsew")
self.heart_value=ctk.CTkLabel(self.heart_frame,text=str(trg_count),width=50,text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway",size=18,weight='bold'))
self.heart_value.grid(row=1,column=0,padx=(20,0),sticky="nsew")
self.mage_label = ctk.CTkLabel(self.user_frame, text="Male Avg Age ", width=50, text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway", size=14))
self.mage_label.grid(row=0, column=0, padx=(10, 0),sticky="nsew")
self.mage_value = ctk.CTkLabel(self.user_frame, text=str(round(male_age_avg,1)), width=50, text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway", size=18, weight='bold'))
self.mage_value.grid(row=1, column=0, padx=(10, 0),pady=(10,0), sticky="nsew")
self.wage_label = ctk.CTkLabel(self.user_frame, text=("Female Avg Age "), width=50, text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway", size=14))
self.wage_label.grid(row=2, column=0, padx=(10, 0), pady=(20, 0))
self.wage_value = ctk.CTkLabel(self.user_frame, text=str(round(female_age_avg,1)), width=50, text_color=(self.home_text_light,self.home_text_dark), font=ctk.CTkFont("Raleway", size=18, weight='bold'))
self.wage_value.grid(row=3, column=0, padx=(20, 0),pady=(10,0), sticky="nsew")
self.blood_label = ctk.CTkLabel(self.acc_frame, text="Fast Blood Sugar", width=50, text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway", size=16))
self.blood_label.grid(row=0, column=0, padx=(10, 0), pady=(20, 0))
self.blood_value = ctk.CTkLabel(self.acc_frame, text=str(fbs_count), width=50, text_color=(self.home_text_light,self.home_text_dark), font=ctk.CTkFont("Raleway", size=18, weight='bold'))
self.blood_value.grid(row=1, column=0, padx=(10, 0),pady=(10,0), sticky="nsew")
# Mean,STD for Age,Cholesterol,Female
# Define the features of interest
features = ['age', 'chol', 'trestbps']
# Calculating means, standard deviations, and variances for male
male_stats = {}
for feature in features:
mean_male = male_data[feature].mean()
std_dev_male = male_data[feature].std()
variance_male = male_data[feature].var()
male_stats[feature] = {'Mean': mean_male, 'Std Dev': std_dev_male, 'Variance': variance_male}
# Calculating means, standard deviations, and variances for female
female_stats = {}
for feature in features:
mean_female = female_data[feature].mean()
std_dev_female = female_data[feature].std()
variance_female = female_data[feature].var()
female_stats[feature] = {'Mean': mean_female, 'Std Dev': std_dev_female, 'Variance': variance_female}
self.mage_basic_label = ctk.CTkLabel(self.pie_tab2, text=(f"Male Age\n\nStd Dev: {male_stats['age']['Std Dev']:.2f}\n\n Variance: {male_stats['age']['Variance']:.2f}\n\n"), width=50, text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway", size=16,weight='bold'))
self.mage_basic_label.grid(row=0, column=0, padx=(10, 0), pady=(20, 0))
self.mchol_basic_label = ctk.CTkLabel(self.pie_tab2, text=(f"Male Cholestrol\n\nMean: {male_stats['chol']['Mean']:.2f}\n\nStd Dev: {male_stats['chol']['Std Dev']:.2f}\n\n Variance: {male_stats['chol']['Variance']:.2f}\n\n"), width=50, text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway", size=16,weight='bold'))
self.mchol_basic_label.grid(row=1, column=0, padx=(10, 0), pady=(20, 0))
self.mbp_basic_label = ctk.CTkLabel(self.chart_tab2, text=(f"Male trestbp\n\nMean: {male_stats['trestbps']['Mean']:.2f}\n\nStd Dev: {male_stats['trestbps']['Std Dev']:.2f}\n\n Variance: {male_stats['trestbps']['Variance']:.2f}\n\n"), width=50, text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway", size=16,weight='bold'))
self.mbp_basic_label.grid(row=0, column=2, padx=(10, 0), pady=(20, 0))
self.fage_basic_label = ctk.CTkLabel(self.pie_tab2, text=(f"Female Cholestrol\n\nStd Dev: {female_stats['age']['Std Dev']:.2f}\n\n Variance: {female_stats['age']['Variance']:.2f}\n\n"), width=50, text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway", size=16,weight='bold'))
self.fage_basic_label.grid(row=0, column=3, padx=(10, 0), pady=(20, 0))
self.fchol_basic_label = ctk.CTkLabel(self.pie_tab2, text=(f"Female Cholestrol\n\nMean: {female_stats['chol']['Mean']:.2f}\n\nStd Dev: {female_stats['chol']['Std Dev']:.2f}\n\n Variance: {female_stats['chol']['Variance']:.2f}\n\n"), width=50, text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway", size=16,weight='bold'))
self.fchol_basic_label.grid(row=1, column=3, padx=(10, 0), pady=(20, 0))
self.fbp_basic_label = ctk.CTkLabel(self.chart_tab2, text=(f"Female trestbp\n\nMean: {female_stats['trestbps']['Mean']:.2f}\n\nStd Dev: {female_stats['trestbps']['Std Dev']:.2f}\n\n Variance: {female_stats['trestbps']['Variance']:.2f}\n\n"), width=50, text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway", size=16,weight='bold'))
self.fbp_basic_label.grid(row=1, column=2, padx=(10, 0), pady=(20, 0))
# Confidence Intervals
for index, row in basic_measures_df.iterrows():
basic_measure = row['Basic Measures']
value = row['Values']
if 'Male' in basic_measure:
count_data = data.shape[0]
count_ci = self.calculate_ci_for_count(int(value.split()[0]), count_data, self.confidence_level)
basic_measure_text = (f"Male : {value}")
confi_text = (f"(Confidence Interval: {count_ci}")
self.male_confi_label = ctk.CTkLabel(self.chart_tab3, text=basic_measure_text, width=50,
text_color=(self.home_text_light, self.home_text_dark),
font=ctk.CTkFont("Raleway", size=16))
self.male_confi_label.grid(row=0, column=0, padx=(10, 0), pady=(10, 0))
self.male_confi_value = ctk.CTkLabel(self.chart_tab3, text=confi_text, width=50,
text_color=(self.home_text_light, self.home_text_dark),
font=ctk.CTkFont("Raleway", size=18, weight='bold'))
self.male_confi_value.grid(row=1, column=0, padx=(20, 0), pady=(10, 0), sticky="nsew")
elif 'Female' in basic_measure:
count_data = data.shape[0]
count_ci = self.calculate_ci_for_count(int(value.split()[0]), count_data, self.confidence_level)
basic_measure_text = (f"Female: {value}")
confi_text = (f"(Confidence Interval: {count_ci}")
self.female_confi_label = ctk.CTkLabel(self.chart_tab3, text=basic_measure_text, width=50,
text_color=(self.home_text_light, self.home_text_dark),
font=ctk.CTkFont("Raleway", size=16))
self.female_confi_label.grid(row=2, column=0, padx=(10, 0), pady=(10, 0))
self.female_confi_value = ctk.CTkLabel(self.chart_tab3, text=confi_text, width=50,
text_color=(self.home_text_light, self.home_text_dark),
font=ctk.CTkFont("Raleway", size=18, weight='bold'))
self.female_confi_value.grid(row=3, column=0, padx=(20, 0), pady=(10, 0), sticky="nsew")
cholesterol_ci = self.calculate_ci_for_mean(data['chol'], self.confidence_level)
basic_measure_text = (f"Avg Cholesterol: {oa_chol:.0f} mg/dL)")
confi_text = (f"(Confidence Interval: {cholesterol_ci}")
self.chol_confi_label = ctk.CTkLabel(self.chart_tab3, text=basic_measure_text, width=50,
text_color=(self.home_text_light, self.home_text_dark),
font=ctk.CTkFont("Raleway", size=16))
self.chol_confi_label.grid(row=4, column=0, padx=(10, 0), pady=(10, 0))
self.chol_confi_value = ctk.CTkLabel(self.chart_tab3, text=confi_text, width=50,
text_color=(self.home_text_light, self.home_text_dark),
font=ctk.CTkFont("Raleway", size=18, weight='bold'))
self.chol_confi_value.grid(row=5, column=0, padx=(20, 0), pady=(10, 0), sticky="nsew")
self.countplot_heart_disease()
self.pie_chart()
#Accuracy of SVC
selected_features = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak',
'slope', 'ca', 'thal']
X = data[selected_features]
Y = data['target']
# Splitting the data into training and testing sets
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# Creating and training the Support Vector Classifier (SVC) model
svc_model = SVC(kernel='linear', C=1.0)
svc_model.fit(X_train_scaled, Y_train)
# Calculate accuracy score for SVC Model
Y_test_pred_svc = svc_model.predict(X_test_scaled)
accuracy_svc = accuracy_score(Y_test, Y_test_pred_svc)
self.acc_label = ctk.CTkLabel(self.acc_frame, text="Accuracy", width=50, text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont("Raleway", size=16))
self.acc_label.grid(row=2, column=0, padx=(10, 0), pady=(20, 0))
self.acc_value = ctk.CTkLabel(self.acc_frame, text=str(round(accuracy_svc*100,1)), width=50, text_color=(self.home_text_light,self.home_text_dark), font=ctk.CTkFont("Raleway", size=18, weight='bold'))
self.acc_value.grid(row=3, column=0, padx=(10, 0),pady=(10,0), sticky="nsew")
def show_data_viewer(self):
if 'self.content_frame' in globals() and self.content_frame.winfo_exists():
for widget in self.content_frame.winfo_children():
widget.destroy()
self.content_frame.destroy()
if 'self.predict_frame' in globals() and self.predict_frame.winfo_children():
for widget in self.predict_tab1.winfo_children():
widget.destroy()
self.predict_frame.destroy()
# Create a new frame for data viewer
self.content_frame1 = ctk.CTkFrame(self.root, corner_radius=0,
fg_color=(self.content_frame1_light, self.content_frame1_dark))
self.content_frame1.grid(row=0, column=1, columnspan=15, rowspan=7, sticky="nsew")
self.data_tabview=ctk.CTkTabview(self.content_frame1,corner_radius=30,width=830,height=600,text_color="black",fg_color=(self.content_frame1_light,self.content_frame1_dark),segmented_button_fg_color="white",segmented_button_selected_hover_color="#F5F1ED",segmented_button_unselected_color="white",segmented_button_selected_color="#F5F1ED")
self.data_tabview.grid(row=0,column=0,padx=(10,0),pady=(10,0),sticky="nsew")
self.datatab1=self.data_tabview.add('Tab 1')
self.datatab2 = self.data_tabview.add('Tab 2')
self.datatab3 = self.data_tabview.add('Tab 3')
self.datatab4 = self.data_tabview.add('Tab 4')
self.datatab5 = self.data_tabview.add('Tab 5')
self.datatab6 = self.data_tabview.add('Tab 6')
self.datatab2.grid_rowconfigure(0,weight=1)
self.subtabview2=ctk.CTkTabview(self.datatab2,corner_radius=30,width=250,height=250,text_color="black",fg_color=(self.home_frames_light,self.home_frames_dark),segmented_button_fg_color="white",segmented_button_selected_hover_color="#F5F1ED",segmented_button_unselected_color="white",segmented_button_selected_color="#F5F1ED")
self.subtabview2.grid(row=0,column=0,padx=10)
self.subtab1=self.subtabview2.add('Tab 1')
self.subtab2=self.subtabview2.add('Tab 2')
self.subtabview3 = ctk.CTkTabview(self.datatab2, corner_radius=30, width=250, height=250, text_color="black",fg_color=(self.home_frames_light,self.home_frames_dark), segmented_button_fg_color="white",segmented_button_selected_hover_color="#F5F1ED",segmented_button_unselected_color="white",segmented_button_selected_color="#F5F1ED")
self.subtabview3.grid(row=0, column=4, padx=10)
self.subtab3 = self.subtabview3.add('Tab 1')
self.subtab4 = self.subtabview3.add('Tab 2')
self.subtabview4=ctk.CTkTabview(self.datatab4, corner_radius=30, width=250, height=250, text_color="black",fg_color=(self.home_frames_light,self.home_frames_dark), segmented_button_fg_color="white",segmented_button_selected_hover_color="#F5F1ED",segmented_button_unselected_color="white",segmented_button_selected_color="#F5F1ED")
self.subtabview4.grid(row=0, column=4, padx=10)
self.subtab5=self.subtabview4.add('Tab 1')
self.subtab6 = self.subtabview4.add('Tab 2')
self.histogram_heart_agewise()
self.countplot_sex()
self.countplot_gender()
self.countplot_chestpain_prone()
self.countplot_fbs_target()
self.histogram_chol_person()
self.hist_chol_density()
self.hist_chol_gender()
self.histplot_chol_heart()
self.heatmap_corr()
self.hist_norm_age()
def predict_model(self):
if 'self.content_frame' in globals() and self.content_frame.winfo_exists():
for widget in self.content_frame.winfo_children():
widget.destroy()
self.content_frame.destroy()
if 'self.content_frame1' in globals() and self.content_frame1.winfo_children():
for widget in self.content_frame1.winfo_children():
widget.destroy()
self.content_frame1.destroy()
if 'self.model_tabview' in globals() and self.model_tabview.winfo_children():
for widget in self.model_tabview.winfo_children():
widget.destroy()
self.model_tabview.destroy()
# Create a new frame for prediction
self.predict_frame = ctk.CTkFrame(self.root,corner_radius=0,width=800,height=400,fg_color=(self.home_frames_light,self.home_frames_dark))
self.predict_frame.grid(row=0, column=1, columnspan=15, rowspan=7, sticky="nsew")
self.predict_frame.grid_rowconfigure(0,weight=1)
self.predict_frame.grid_columnconfigure(0,weight=1)
self.predict_tab=ctk.CTkTabview(self.predict_frame,corner_radius=10,fg_color=("white",self.content_frame1_dark),text_color="black",segmented_button_fg_color="white",segmented_button_selected_hover_color="#F5F1ED",segmented_button_unselected_color="white",segmented_button_selected_color="#F5F1ED")
self.predict_tab.grid(row=0,column=0,padx=10,pady=10,sticky="nsew")
self.predict_tab1=self.predict_tab.add('Tab 1')
self.predict_tab2=self.predict_tab.add('Tab 2')
self.predict_tab2.grid_rowconfigure(0,weight=1)
self.heading_label=ctk.CTkLabel(self.predict_tab1,text="Prediction",width=50,height=50,text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont('Raleway',size=24,weight='bold'))
self.heading_label.grid(row=0,column=0,padx=10,pady=10)
self.tab2frame=ctk.CTkFrame(self.predict_tab2,corner_radius=10,fg_color=("white",self.content_frame1_dark))
self.tab2frame.grid(row=0,column=0,padx=10,pady=10,sticky="nsew")
self.tab2frame.grid_rowconfigure((0,3),weight=1)
self.age_label=ctk.CTkLabel(self.predict_tab1,text="Age:",text_color=(self.home_text_light,self.home_text_dark),width=50,font=ctk.CTkFont('Raleway',size=16))
self.age_label.grid(row=2,column=0,padx=(10,0),pady=(10,0))
self.age_entry=ctk.CTkEntry(self.predict_tab1,placeholder_text="Enter Age",width=300,height=50,border_width=2,corner_radius=10,fg_color=(self.home_frames_light,self.home_frames_dark))
self.age_entry.grid(row=2,column=1,columnspan=2,padx=(10,0),pady=(10,0))
self.sex_label = ctk.CTkLabel(self.predict_tab1, text="Gender:",text_color=(self.home_text_light, self.home_text_dark), width=50,font=ctk.CTkFont('Raleway', size=16))
self.sex_label.grid(row=3, column=0, padx=(10, 0), pady=(10, 0))
self.sex_entry = ctk.CTkEntry(self.predict_tab1, placeholder_text="Enter Male or Female", width=300, height=50, border_width=2, corner_radius=10,fg_color=(self.home_frames_light, self.home_frames_dark))
self.sex_entry.grid(row=3, column=1,columnspan=2, padx=(10, 0), pady=(10, 0))
self.cp_label = ctk.CTkLabel(self.predict_tab1, text="Chest Pain:",text_color=(self.home_text_light, self.home_text_dark), width=50,font=ctk.CTkFont('Raleway', size=16))
self.cp_label.grid(row=4, column=0, padx=(10, 0), pady=(10, 0))
self.cp_menu = ctk.CTkOptionMenu(self.predict_tab1,values=["Typical_angina","Atypical_angina","Non_anginal","Asymptomatic"], width=300, corner_radius=5,fg_color=(self.home_frames_light, self.home_frames_dark),text_color=(self.sidebar_button_text_light,self.sidebar_button_text_dark),dropdown_fg_color=self.sidebar_light)
self.cp_menu.grid(row=4, column=1,columnspan=2, padx=(10, 0), pady=(10, 0))
self.trest_label = ctk.CTkLabel(self.predict_tab1, text="Blood Pressure:",text_color=(self.home_text_light, self.home_text_dark), width=50,font=ctk.CTkFont('Raleway', size=16))
self.trest_label.grid(row=5, column=0, padx=(10, 0), pady=(10, 0))
self.trest_entry = ctk.CTkEntry(self.predict_tab1, placeholder_text="Enter BP", width=300, height=50, border_width=2, corner_radius=10,fg_color=(self.home_frames_light, self.home_frames_dark))
self.trest_entry.grid(row=5, column=1,columnspan=2, padx=(10, 0), pady=(10, 0))
self.chol_label = ctk.CTkLabel(self.predict_tab1, text="Cholestrol:",text_color=(self.home_text_light, self.home_text_dark), width=50,font=ctk.CTkFont('Raleway', size=16))
self.chol_label.grid(row=6, column=0, padx=(10, 0), pady=(10, 0))
self.chol_entry = ctk.CTkEntry(self.predict_tab1, placeholder_text="Enter Chol", width=300, height=50, border_width=2, corner_radius=10,fg_color=(self.home_frames_light, self.home_frames_dark))
self.chol_entry.grid(row=6, column=1,columnspan=2, padx=(10, 0), pady=(10, 0))
self.fbs_label = ctk.CTkLabel(self.predict_tab1, text="Fast BP(>120):",text_color=(self.home_text_light, self.home_text_dark), width=50,font=ctk.CTkFont('Raleway', size=16))
self.fbs_label.grid(row=7, column=0, padx=(10, 0), pady=(10, 0))
self.fbs_menu = ctk.CTkOptionMenu(self.predict_tab1,values=["Yes","No"], width=300, corner_radius=5,fg_color=(self.home_frames_light, self.home_frames_dark),text_color=(self.sidebar_button_text_light,self.sidebar_button_text_dark),dropdown_fg_color=self.sidebar_light)
self.fbs_menu.grid(row=7, column=1,columnspan=2, padx=(10, 0), pady=(10, 0))
self.cm_label = ctk.CTkLabel(self.predict_tab1, text="Cardiography:",text_color=(self.home_text_light, self.home_text_dark), width=50,font=ctk.CTkFont('Raleway', size=16))
self.cm_label.grid(row=8, column=0, padx=(10, 0), pady=(10, 0))
self.cm_menu = ctk.CTkOptionMenu(self.predict_tab1,values=["Normal","Abnormal","Hypertropy"], width=300, corner_radius=5,fg_color=(self.home_frames_light, self.home_frames_dark),text_color=(self.sidebar_button_text_light,self.sidebar_button_text_dark),dropdown_fg_color=self.sidebar_light)
self.cm_menu.grid(row=8, column=1,columnspan=2, padx=(10, 0), pady=(10, 0))
self.ch_label = ctk.CTkLabel(self.predict_tab1, text="Heart Rate(max):",text_color=(self.home_text_light, self.home_text_dark), width=50,font=ctk.CTkFont('Raleway', size=16))
self.ch_label.grid(row=9, column=0, padx=(10, 0), pady=(10, 0))
self.ch_entry = ctk.CTkEntry(self.predict_tab1, placeholder_text="Enter Heart Rate", width=300, height=50, border_width=2, corner_radius=10,fg_color=(self.home_frames_light, self.home_frames_dark))
self.ch_entry.grid(row=9, column=1,columnspan=2, padx=(10, 0), pady=(10, 0))
self.ng_label = ctk.CTkLabel(self.predict_tab1, text="Induced Angina:",text_color=(self.home_text_light, self.home_text_dark), width=50,font=ctk.CTkFont('Raleway', size=16))
self.ng_label.grid(row=2, column=5, padx=(10, 0), pady=(10, 0))
self.ng_menu = ctk.CTkOptionMenu(self.predict_tab1,values=["Yes","No"], width=300, corner_radius=5,fg_color=(self.home_frames_light, self.home_frames_dark),text_color=(self.sidebar_button_text_light,self.sidebar_button_text_dark),dropdown_fg_color=self.sidebar_light)
self.ng_menu.grid(row=2, column=6,columnspan=2, padx=(10, 0), pady=(10, 0))
self.old_label = ctk.CTkLabel(self.predict_tab1, text="ST Depression:",text_color=(self.home_text_light, self.home_text_dark), width=50,font=ctk.CTkFont('Raleway', size=16))
self.old_label.grid(row=3, column=5, padx=(20, 0), pady=(10, 0))
self.old_entry = ctk.CTkEntry(self.predict_tab1, placeholder_text="Enter ST", width=300, height=50, border_width=2, corner_radius=10,fg_color=(self.home_frames_light, self.home_frames_dark))
self.old_entry.grid(row=3, column=6,columnspan=2, padx=(10, 0), pady=(10, 0))
self.cg_label = ctk.CTkLabel(self.predict_tab1, text="ST Slope:",text_color=(self.home_text_light, self.home_text_dark), width=50,font=ctk.CTkFont('Raleway', size=16))
self.cg_label.grid(row=4, column=5, padx=(10, 0), pady=(10, 0))
self.cg_menu = ctk.CTkOptionMenu(self.predict_tab1,values=["Upsloping","Flat","Downsloping"], width=300, corner_radius=5,fg_color=(self.home_frames_light, self.home_frames_dark),text_color=(self.sidebar_button_text_light,self.sidebar_button_text_dark),dropdown_fg_color=self.sidebar_light)
self.cg_menu.grid(row=4, column=6,columnspan=2, padx=(10, 0), pady=(10, 0))
self.ca_label = ctk.CTkLabel(self.predict_tab1, text="Blood Vessels:",text_color=(self.home_text_light, self.home_text_dark), width=50,font=ctk.CTkFont('Raleway', size=16))
self.ca_label.grid(row=5, column=5, padx=(10, 0), pady=(10, 0))
self.ca_menu = ctk.CTkOptionMenu(self.predict_tab1,values=["0","1","2","3","4"], width=300, corner_radius=5,fg_color=(self.home_frames_light, self.home_frames_dark),text_color=(self.sidebar_button_text_light,self.sidebar_button_text_dark),dropdown_fg_color=self.sidebar_light)
self.ca_menu.grid(row=5, column=6,columnspan=2, padx=(10, 0), pady=(10, 0))
self.thal_label = ctk.CTkLabel(self.predict_tab1, text="Thalassemia:",text_color=(self.home_text_light, self.home_text_dark), width=50,font=ctk.CTkFont('Raleway', size=16))
self.thal_label.grid(row=6, column=5, padx=(10, 0), pady=(10, 0))
self.thal_menu = ctk.CTkOptionMenu(self.predict_tab1,values=["NULL","Defect","Normal","Reversible"], width=300, corner_radius=5,fg_color=(self.home_frames_light, self.home_frames_dark),text_color=(self.sidebar_button_text_light,self.sidebar_button_text_dark),dropdown_fg_color=self.sidebar_light)
self.thal_menu.grid(row=6, column=6,columnspan=2, padx=(10, 0), pady=(10, 0))
self.predict = ctk.CTkButton(self.predict_tab1, corner_radius=10, height=40, border_spacing=10, text=" Predict",fg_color=self.sidebar_buttons_hover_light, text_color=(self.sidebar_button_text_light,self.sidebar_button_text_dark), hover_color=(self.sidebar_buttons_hover_light,self.sidebar_buttons_hover_dark), anchor="w", command=self.svc_logistic,font=ctk.CTkFont("Poppins",size=16,weight="bold"))
self.predict.grid(row=8, column=7, sticky="ew")
#For Linear Regression
self.linear_label=ctk.CTkLabel(self.tab2frame,text="Linear Prediction",width=50,height=50,text_color=(self.home_text_light,self.home_text_dark),font=ctk.CTkFont('Raleway',size=28,weight='bold'))
self.linear_label.grid(row=0,column=3,padx=10,pady=10)
self.age_reg=ctk.CTkLabel(self.tab2frame,text="Age:",text_color=(self.home_text_light,self.home_text_dark),width=50,font=ctk.CTkFont('Raleway',size=16))
self.age_reg.grid(row=2,column=0,padx=(10,0),pady=(10,0))
self.age_reg_entry=ctk.CTkEntry(self.tab2frame,placeholder_text="Enter Age",width=300,height=50,border_width=2,corner_radius=10,fg_color=(self.home_frames_light,self.home_frames_dark))
self.age_reg_entry.grid(row=2,column=1,columnspan=2,padx=(10,0),pady=(10,0))
self.chol_reg = ctk.CTkLabel(self.tab2frame, text="Cholestrol:",text_color=(self.home_text_light, self.home_text_dark), width=50,font=ctk.CTkFont('Raleway', size=16))
self.chol_reg.grid(row=2, column=3, padx=(10, 0), pady=(10, 0))
self.chol_reg_entry = ctk.CTkEntry(self.tab2frame, placeholder_text="Enter Chol", width=300, height=50, border_width=2, corner_radius=10,fg_color=(self.home_frames_light, self.home_frames_dark))
self.chol_reg_entry.grid(row=2, column=4,columnspan=2, padx=(10, 0), pady=(10, 0))
self.predict_reg = ctk.CTkButton(self.tab2frame, corner_radius=10, height=40, border_spacing=10, text=" Predict",fg_color=self.sidebar_buttons_hover_light, text_color=(self.sidebar_button_text_light,self.sidebar_button_text_dark), hover_color=(self.sidebar_buttons_hover_light,self.sidebar_buttons_hover_dark), anchor="w", command=self.linear_model,font=ctk.CTkFont("Poppins",size=16,weight="bold"))
self.predict_reg.grid(row=3, column=3,sticky="ew")
# DataFrame
confidence_level = 0.95
# Calculating confidence interval for mean
def calculate_ci_for_mean(self,data, confidence_level):
mean = np.mean(data)
std_dev = np.std(data)
n = len(data)
margin_of_error = stats.norm.ppf((1 + confidence_level) / 2) * (std_dev / np.sqrt(n))
lower_bound = round(mean - margin_of_error,2)
upper_bound = round(mean + margin_of_error,2)
return lower_bound, upper_bound
def calculate_ci_for_count(self,count, total, confidence_level):
p = count / total
margin_of_error = stats.norm.ppf((1 + confidence_level) / 2) * np.sqrt((p * (1 - p)) / total)
lower_bound = round(p - margin_of_error,2)
upper_bound = round(p + margin_of_error,2)
return lower_bound, upper_bound
def linear_model(self):
# Linear Regression for predicting cholesterol level based on age
# Feature selection for linear regression
selected_features = ['age', 'chol']
X_chol = data[selected_features]
Y_chol = data['chol']
# Splitting the data into training and testing sets
X_train_chol, X_test_chol, Y_train_chol, Y_test_chol = train_test_split(X_chol, Y_chol, test_size=0.2,
random_state=2)
# Creating and training the linear regression model
linear_model_chol = LinearRegression()
linear_model_chol.fit(X_train_chol, Y_train_chol)
# Predicting cholesterol levels
self.age_linear = self.age_reg_entry.get()
self.chol_linear = self.chol_reg_entry.get()
if self.age_linear and self.chol_linear:
new_data_chol = np.array([[int(self.age_linear),int(self.chol_linear)]]) # Replace with input values
linear_prediction_chol = linear_model_chol.predict(new_data_chol)
for widget in self.predict_tab2.winfo_children():
widget.destroy()
# Scatter plot and regression line for Age vs Cholesterol using linear regression
fig1,ax1=plt.subplots(figsize=(5, 5))
sns.regplot(x=X_test_chol['age'], y=Y_test_chol,
scatter_kws={'color': 'red', 'label': 'Cholesterol Level (Actual)'}, line_kws={'color': 'blue'},
ci=None, marker=None)
ax1.set_title('Age vs Cholesterol Level (Linear Regression)')
ax1.set_xlabel('Age')
ax1.set_ylabel('Cholesterol Level')
canvas1 = FigureCanvasTkAgg(fig1, master=self.predict_tab2)
canvas1.draw()
canvas1.get_tk_widget().grid(row=0, column=0, padx=20)
self.chol_predict_label=ctk.CTkLabel(self.predict_tab2,text=(f"Predicted Cholesterol Level: {linear_prediction_chol[0]:.2f} mg/dL\n"),text_color=(self.home_text_light, self.home_text_dark), width=50,font=ctk.CTkFont('Raleway', size=16))
self.chol_predict_label.grid(row=0, column=3, padx=(10, 0), pady=(10, 0))
# Predicting cholesterol levels on the test set
y_pred_chol = linear_model_chol.predict(X_test_chol)
# Calculate R-squared score
r2 = r2_score(Y_test_chol, y_pred_chol)
self.r2_predict_value=ctk.CTkLabel(self.predict_tab2,text=(f"R-squared Score: {r2:.4f}"),text_color=(self.home_text_light, self.home_text_dark), width=50,font=ctk.CTkFont('Raleway', size=16))
self.r2_predict_value.grid(row=1, column=3, padx=(10, 0), pady=(10, 0)) # this is accuracy denoted as R-2 Score
def svc_logistic(self):
selected_features = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak',
'slope', 'ca', 'thal']
X = data[selected_features]
Y = data['target']
# Splitting the data into training and testing sets
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
# Creating and training the logistic regression model
model = LogisticRegression(max_iter=1000)
model.fit(X_train_scaled, Y_train)
X_test_scaled = scaler.transform(X_test)
# Creating and training the Support Vector Classifier (SVC) model
svc_model = SVC(kernel='linear', C=1.0)
svc_model.fit(X_train_scaled, Y_train)
self.age = self.age_entry.get()
self.gender = self.sex_entry.get()
self.cp = self.cp_menu.get()
self.trest = self.trest_entry.get()
self.chol = self.chol_entry.get()
self.fbs = self.fbs_menu.get()
self.cm = self.cm_menu.get()
self.ch = self.ch_entry.get()
self.ng=self.ng_menu.get()
self.old=self.old_entry.get()
self.cg=self.cg_menu.get()
self.ca=self.ca_menu.get()
self.thal=self.thal_menu.get()
if self.gender == "Male":
self.gender = "1"
elif self.gender == "Female":
self.gender = "0"
if self.cp == "Typical_angina":
self.cp = "0"
elif self.cp == "Atypical_angina":
self.cp = "1"
elif self.cp == "Non_anginal":
self.cp = "2"
elif self.cp == "Asymptomatic":
self.cp = "3"
if self.cm=="Normal":
self.cm="0"
elif self.cm=="Abnormal":
self.cm="1"
elif self.cm=="Hypertropy":
self.cm:"2"
if self.fbs == "Yes":
self.fbs = "1"
elif self.fbs == "No":
self.fbs = "0"
if self.cg == "Upsloping":
self.cg = "0"
elif self.cg == "Flat":
self.cg = "1"
elif self.cg =="Downsloping":
self.cg = "2"
if self.ng=="Yes":
self.ng="1"
elif self.ng=="No":
self.ng="0"
if self.thal=="NULL":
self.thal="0"
elif self.thal=="Defect":
self.thal="1"
elif self.thal=="Normal":
self.thal="2"
elif self.thal=="Reversible":
self.thal="3"
# Check that all values are not empty
if self.age and self.gender and self.cp and self.trest and self.chol and self.fbs and self.cg and self.ch:
# Convert the values to integers and create the array
new_data = np.array([[int(self.age), int(self.gender), int(self.cp), int(self.trest), int(self.chol), int(self.fbs), int(self.cm), int(self.ch), int(self.ng), float(self.old),int(self.cg),int(self.ca),int(self.thal)]])
new_data_scaled = scaler.transform(new_data)
prediction = model.predict(new_data_scaled)
svc_prediction=svc_model.predict(new_data_scaled)
for widget in self.predict_tab1.winfo_children():
widget.destroy()
self.model_tabview = ctk.CTkTabview(self.predict_tab1, corner_radius=30, width=830, height=600,text_color="black",fg_color=(self.home_frames_light, self.home_frames_dark),segmented_button_fg_color="white",segmented_button_selected_hover_color="#F5F1ED",segmented_button_unselected_color="white",segmented_button_selected_color="#F5F1ED")
self.model_tabview.grid(row=0, column=1, padx=(10, 0), pady=(10, 0), sticky="nsew")
self.modtab2=self.model_tabview.add('Logistic')
self.modtab3 = self.model_tabview.add('SVC')
self.model_subtabview = ctk.CTkTabview(self.modtab3, corner_radius=30,
text_color="black",
fg_color=(self.home_frames_light, self.home_frames_dark),
segmented_button_fg_color="white",
segmented_button_selected_hover_color="#F5F1ED",
segmented_button_unselected_color="white",
segmented_button_selected_color="#F5F1ED")
self.model_subtabview.grid(row=4, column=3, padx=(10, 0), pady=(10, 0), sticky="nsew")
self.submodtab1=self.model_subtabview.add('ZScore')
self.submodtab2 = self.model_subtabview.add('Age')
self.submodtab3 = self.model_subtabview.add('Chestpain')
self.submodtab4=self.model_subtabview.add('Sex')
self.modtab4.grid_columnconfigure(6,weight=1)
self.confi_frame=ctk.CTkFrame(self.modtab4,corner_radius=30,fg_color=(self.home_frames_light,self.home_frames_dark))
self.confi_frame.grid(row=0, column=6,columnspan=4,rowspan=5,sticky="nsew")
# # Scatter plot and regression line with Z-Score of Age vs Heart Disease
fig2,ax2=plt.subplots(figsize=(5, 5))
plt.scatter(zscore(X_test['age']), Y_test, c='red', label='Heart Problem')
sns.regplot(x=zscore(X_test['age']), y=model.predict(X_test), data=data, logistic=True, ci=None,
scatter=False, color='blue', line_kws={'color': 'blue'})
ax2.set_title('Age vs Heart Disease with Z-Score')
ax2.set_xlabel('Age Z-score')
ax2.set_ylabel('Heart Disease')
canvas2 = FigureCanvasTkAgg(fig2, master=self.modtab2)
canvas2.draw()
canvas2.get_tk_widget().grid(row=0, column=0, padx=20)
# logistics
if prediction[0] == 0:
log_text = "Logistic Regression:\n This Person does not have Heart Disease"
self.log_label = ctk.CTkLabel(self.modtab2, text=log_text,
text_color=(self.home_text_light, self.home_text_dark), width=50,
font=ctk.CTkFont('Raleway', size=16))
self.log_label.grid(row=0, column=3, padx=(10, 0), pady=(10, 0))
else:
log_text = "Logistic Regression:\n This Person have Heart Disease"
self.log_label = ctk.CTkLabel(self.modtab2, text=log_text,
text_color=(self.home_text_light, self.home_text_dark), width=50,
font=ctk.CTkFont('Raleway', size=16))
self.log_label.grid(row=0, column=3, padx=(10, 0), pady=(10, 0))
# SVC
# Scatter plot and regression line with Z-Score of Age vs Heart Disease
fig3,ax3=plt.subplots(figsize=(8,5))
plt.scatter(X_test['age'], Y_test, c='red', label='Heart Problem')
sns.regplot(x=X_test['age'], y=svc_model.predict(X_test_scaled), data=data, ci=None, scatter=False,
color='blue', line_kws={'color': 'blue'})
ax3.set_title('Age vs Heart Disease with SVC')
ax3.set_xlabel('Age')
ax3.set_ylabel('Heart Disease')
canvas3 = FigureCanvasTkAgg(fig3, master=self.submodtab1)
canvas3.draw()
canvas3.get_tk_widget().grid(row=0, column=0, padx=20)
# Scatter plot and regression line for Age vs Heart Disease
fig4, ax4 = plt.subplots(figsize=(8, 5))
plt.scatter(X_test['age'], Y_test, c='red', label='Heart Problem')
sns.regplot(x=X_test['age'], y=svc_model.predict(X_test), data=data, logistic=True, ci=None,scatter=False, color='blue', line_kws={'color': 'blue'})
ax4.set_title('Age vs Heart Disease with SVC')
ax4.set_xlabel('Age')
ax4.set_ylabel('Heart Disease')
canvas4 = FigureCanvasTkAgg(fig4, master=self.submodtab2)
canvas4.draw()
canvas4.get_tk_widget().grid(row=0, column=0, padx=20)
# Scatter plot and regression line for Chest-Pain vs Heart Disease
fig5, ax5 = plt.subplots(figsize=(8, 5))
plt.scatter(X_test['cp'], Y_test, c='red', label='Heart Problem')
sns.regplot(x=X_test['cp'], y=svc_model.predict(X_test), data=data, logistic=True, ci=None, scatter=False,
color='blue', line_kws={'color': 'blue'})
ax5.set_title('Chest-Pain vs Heart Disease with SVC')
ax5.set_xlabel('Chest-Pain')
ax5.set_ylabel('Heart Disease')
canvas5 = FigureCanvasTkAgg(fig5, master=self.submodtab3)
canvas5.draw()
canvas5.get_tk_widget().grid(row=0, column=0, padx=20)
# Scatter plot and regression line for Sex vs Heart Disease
fig6, ax6 = plt.subplots(figsize=(8, 5))
plt.scatter(X_test['sex'], Y_test, c='red', label='Heart Problem')
sns.regplot(x=X_test['sex'], y=svc_model.predict(X_test), data=data, logistic=True, ci=None,
scatter=False, color='blue', line_kws={'color': 'blue'})
ax6.set_title('Sex vs Heart Disease with SVC')
ax6.set_xlabel('Sex')
ax6.set_ylabel('Heart Disease')
canvas6 = FigureCanvasTkAgg(fig6, master=self.submodtab4)
canvas6.draw()
canvas6.get_tk_widget().grid(row=0, column=0, padx=20)
# if svc_prediction[0] == 0:
# svc_text = "Support Vector Classifier:\n This Person does not have Heart Disease"
# self.svc_label = ctk.CTkLabel(self.modtab3, text=svc_text,
# text_color=(self.home_text_light, self.home_text_dark), width=50,
# font=ctk.CTkFont('Raleway', size=16))
# self.svc_label.grid(row=0, column=3, padx=(10, 0), pady=(10, 0))
# else:
# svc_text = "Support Vector Classifier:\n This Person have Heart Disease"
# self.svc_label = ctk.CTkLabel(self.modtab3, text=svc_text,
# text_color=(self.home_text_light, self.home_text_dark), width=50,
# font=ctk.CTkFont('Raleway', size=16))
# self.svc_label.grid(row=0, column=3, padx=(10, 0), pady=(10, 0))
# # Calculate accuracy score for Logistic Regression
# Y_test_pred = model.predict(X_test)
# accuracy_logistic_regression = accuracy_score(Y_test, Y_test_pred)
#
#
# # Convert predicted probabilities to binary outcomes (0 or 1)
# Y_test_pred_binary = (Y_test_pred >= 0.5).astype(int)
# # Convert the true labels to binary outcomes for comparison
# Y_test_binary = (Y_test == 1).astype(int)
#
# # Calculate accuracy score for SVC Model
# Y_test_pred_svc = svc_model.predict(X_test_scaled)
# accuracy_svc = accuracy_score(Y_test, Y_test_pred_svc)
#
# # Plotting Bar Graph to compare Models
# models = ['Linear', 'Logistic', 'SVC']
#
# # Accuracy scores
# accuracy_scores = [accuracy_logistic_regression, accuracy_svc]
# fig7, ax7 = plt.subplots(figsize=(5,5))
# plt.bar(models, accuracy_scores, color=['orange', 'green'])
# ax7.set_ylim(0, 1) # Set the y-axis limit from 0 to 1 for accuracy scores
# ax7.set_title('Comparison of Accuracy Scores')
# ax7.set_xlabel('Models')
# ax7.set_ylabel('Accuracy Score')
# ax7.set_ylabel('Heart Disease')
# canvas7 = FigureCanvasTkAgg(fig7, master=self.modtab4)
# canvas7.draw()
# canvas7.get_tk_widget().grid(row=0, column=0, padx=20)
else:
print("Please enter values for all fields.")
# Logistics Model
# def change_appearance_mode_event(self, new_appearance_mode: str):
# ctk.set_appearance_mode(new_appearance_mode)
# appearance_mode=ctk.get_appearance_mode()
# if appearance_mode=="Dark":
# self.histogram_heart_agewise()
# self.countplot_sex()
# self.countplot_gender()
# self.countplot_chestpain_prone()
# self.countplot_fbs_target()
# self.histogram_chol_person()
# # self.histogram_chol()
# self.hist_chol_density()
# self.hist_chol_gender()
# self.histplot_chol_heart()
# self.heatmap_corr()
# self.hist_norm_age()
# self.countplot_heart_disease()
# self.pie_chart()
def countplot_heart_disease(self):
fig1, ax1 = plt.subplots(figsize=(4,4))
sns.countplot(x='target', data=data, ax=ax1)
ax1.set_xticklabels(['less chance', 'more chance'])
ax1.set_title('Chances of Heart Disease')
appear_mode=ctk.get_appearance_mode()
if appear_mode=="Dark":
fig1.set_facecolor(self.home_frames_dark)
ax1.set_facecolor(self.home_frames_dark)
else:
fig1.set_facecolor("none")
ax1.set_facecolor("none")
canvas1 = FigureCanvasTkAgg(fig1, master=self.chart_tab1)
canvas1.draw()
canvas1.get_tk_widget().grid(row=0,column=0,padx=20)
def histogram_heart_agewise(self):
fig2, ax2 = plt.subplots(figsize=(9,6))
data['age'].hist(bins=20, ax=ax2)
ax2.set_title('Number of People Having Heart Disease Age Wise')
ax2.set_xlabel('Age')
ax2.set_ylabel('No. of Persons')
appear_mode = ctk.get_appearance_mode()
if appear_mode == "Dark":
fig2.set_facecolor(self.home_frames_dark)
ax2.set_facecolor(self.home_frames_dark)
else:
fig2.set_facecolor("none")
ax2.set_facecolor("none")
canvas2 = FigureCanvasTkAgg(fig2, master=self.datatab1)
canvas2.draw()
canvas2.get_tk_widget().grid(row=0,column=0)
def countplot_sex(self):
fig3, ax3 = plt.subplots(figsize=(4, 4))
sns.countplot(x='sex', data=data, ax=ax3)
ax3.set_xticklabels(['Females', 'Males'])
ax3.set_title('Number of Males and Females')
appear_mode = ctk.get_appearance_mode()
if appear_mode == "Dark":
fig3.set_facecolor(self.home_frames_dark)
ax3.set_facecolor(self.home_frames_dark)
else:
fig3.set_facecolor("none")
ax3.set_facecolor("none")
canvas1 = FigureCanvasTkAgg(fig3, master=self.subtab1)
canvas1.draw()
canvas1.get_tk_widget().grid(row=0, column=0, padx=20)
def countplot_gender(self):
fig4,ax4 = plt.subplots(figsize=(4, 4))
sns.countplot(x='sex', data=data, hue='target', ax=ax4)
ax4.set_xticklabels(['Females', 'Males'])
ax4.set_title('Chances of Heart Disease Gender Wise')
ax4.legend(labels=['Less Chance', 'High Chance'])
appear_mode = ctk.get_appearance_mode()
if appear_mode == "Dark":
fig4.set_facecolor(self.home_frames_dark)
ax4.set_facecolor(self.home_frames_dark)
else:
fig4.set_facecolor("none")
ax4.set_facecolor("none")
canvas1 = FigureCanvasTkAgg(fig4, master=self.subtab2)
canvas1.draw()
canvas1.get_tk_widget().grid(row=0, column=5, padx=10,pady=10)
def countplot_chestpain_prone(self):
fig5,ax5 = plt.subplots(figsize=(9, 6))
sns.countplot(x='cp', hue='target', data=data, ax=ax5)
ax5.set_xticklabels(["typical angina", "atypical angina", "non-anginal pain", "asymptomatic"])
ax5.set_title('Chest pain and number of people having high or low chances of heart attack')
ax5.legend(labels=['low chance', 'high chance'])
appear_mode = ctk.get_appearance_mode()
if appear_mode == "Dark":
fig5.set_facecolor(self.home_frames_dark)
ax5.set_facecolor(self.home_frames_dark)
else:
fig5.set_facecolor("none")
ax5.set_facecolor("none")
canvas1 = FigureCanvasTkAgg(fig5, master=self.datatab3)
canvas1.draw()
canvas1.get_tk_widget().grid(row=0, column=0, padx=10,pady=10)
def histogram_chol_person(self):
fig7,ax7 = plt.subplots(figsize=(4, 4))
data['chol'].hist(ax=ax7)
ax7.set_xlabel('Serum cholestoral (mg/dl)')
ax7.set_ylabel('Person')
ax7.set_title('Serum Cholesterol Levels in Patients')
appear_mode = ctk.get_appearance_mode()
if appear_mode == "Dark":
fig7.set_facecolor(self.home_frames_dark)
ax7.set_facecolor(self.home_frames_dark)
else:
fig7.set_facecolor("none")
ax7.set_facecolor("none")
canvas1 = FigureCanvasTkAgg(fig7, master=self.subtab3)
canvas1.draw()
canvas1.get_tk_widget().grid(row=0, column=5, padx=20, pady=10)
# def histogram_chol(self):
# fig8,ax8 = plt.subplots(figsize=(4, 4))
# data['chol'].hist( bins=20, color='skyblue', edgecolor='black', ax=ax8)
# ax8.set_title('Serum Cholesterol Levels in Patients')
# ax8.set_xlabel('Serum Cholesterol (mg/dl)')
# ax8.set_ylabel('Number of Persons')
# canvas1 = FigureCanvasTkAgg(fig8, master=self.datatab4)
# canvas1.draw()
# canvas1.get_tk_widget().grid(row=0, column=0, padx=10, pady=10)
def countplot_fbs_target(self):
fig6, ax6 = plt.subplots(figsize=(4, 4))
sns.countplot(x='fbs', hue='target', data=data, ax=ax6)
ax6.set_title('Fasting Blood Sugar and Heart Attack')
ax6.legend(labels=['low chance', 'high chance'])
appear_mode = ctk.get_appearance_mode()
if appear_mode == "Dark":
fig6.set_facecolor(self.home_frames_dark)
ax6.set_facecolor(self.home_frames_dark)
else:
fig6.set_facecolor("none")
ax6.set_facecolor("none")
canvas1 = FigureCanvasTkAgg(fig6, master=self.datatab4)
canvas1.draw()
canvas1.get_tk_widget().grid(row=0, column=0, padx=10, pady=10)
def hist_chol_density(self):
fig9,ax9=plt.subplots(figsize=(4,4))
sns.histplot(data['chol'], kde=True, color='orange', stat='density')
ax9.set_xlabel('Serum Cholestoral (mg/dl)')
ax9.set_ylabel('Probability Density')
ax9.set_title('Cholesterol with Probability Density Curve')
appear_mode = ctk.get_appearance_mode()
if appear_mode == "Dark":
fig9.set_facecolor(self.home_frames_dark)
ax9.set_facecolor(self.home_frames_dark)
else:
fig9.set_facecolor("none")
ax9.set_facecolor("none")
canvas1 = FigureCanvasTkAgg(fig9, master=self.subtab4)
canvas1.draw()
canvas1.get_tk_widget().grid(row=0, column=5, padx=10, pady=10)
def hist_chol_gender(self):
fig10,ax10 = plt.subplots(figsize=(4, 4))
sns.histplot(data=combined_chol_data, x="chol", hue="sex", multiple="stack", ax=ax10, palette='YlGnBu')
ax10.set_title('Cholesterol vs Participant Gender')
ax10.set_xlabel('Cholesterol Value mg/dL')
ax10.set_ylabel('Number of Participants')
ax10.legend(title="Sex", labels=["Male", "Female"])
appear_mode = ctk.get_appearance_mode()
if appear_mode == "Dark":
fig10.set_facecolor(self.home_frames_dark)
ax10.set_facecolor(self.home_frames_dark)
else:
fig10.set_facecolor("none")
ax10.set_facecolor("none")
canvas1 = FigureCanvasTkAgg(fig10, master=self.subtab5)
canvas1.draw()
canvas1.get_tk_widget().grid(row=0, column=0, padx=10, pady=10)
def histplot_chol_heart(self):
fig11,ax11 = plt.subplots(figsize=(4, 4))
sns.histplot(data=combined_chol_data, x="chol", hue="target", multiple="stack", ax=ax11, palette='YlGnBu')
ax11.set_title('Cholesterol vs Heart Disease Diagnosis')
ax11.set_xlabel('Cholesterol Value mg/dL')
ax11.set_ylabel('Number of Participants')
ax11.legend(title="Heart Disease", labels=["No", "Yes"])
appear_mode = ctk.get_appearance_mode()
if appear_mode == "Dark":
fig11.set_facecolor(self.home_frames_dark)
ax11.set_facecolor(self.home_frames_dark)
else:
fig11.set_facecolor("none")
ax11.set_facecolor("none")
canvas1 = FigureCanvasTkAgg(fig11, master=self.subtab6)
canvas1.draw()
canvas1.get_tk_widget().grid(row=0, column=5, padx=10, pady=10)
def pie_chart(self):
fig12, ax12 = plt.subplots(figsize=(4, 4))
labels = heart_percent.keys()
sizes = heart_percent.values()
colors = ["#c1d8c1", "#6283af"]
explode = (0, 0.1)
ax12.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=False, colors=colors, startangle=90,textprops={'fontsize': 10})
ax12.set_title("Heart Disease Percentage", size=10, fontweight="bold")
appear_mode = ctk.get_appearance_mode()
if appear_mode == "Dark":
fig12.set_facecolor(self.home_frames_dark)
ax12.set_facecolor(self.home_frames_dark)
else:
fig12.set_facecolor("none")
ax12.set_facecolor("none")
canvas1 = FigureCanvasTkAgg(fig12, master=self.pie_tab1)
canvas1.draw()
canvas1.get_tk_widget().grid(row=0, column=0, padx=10, pady=10)
def heatmap_corr(self):
fig13,ax13=plt.subplots(figsize=(9,6))
corr_matrix = data.corr()
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=".2f", linewidths=.5, vmin=-1, vmax=1, center=0,cbar_kws={"shrink": 0.75}, ax=ax13)
ax13.set_title('Correlation Heatmap of Updated Features')
appear_mode = ctk.get_appearance_mode()
if appear_mode == "Dark":
fig13.set_facecolor(self.home_frames_dark)
ax13.set_facecolor(self.home_frames_dark)
else:
fig13.set_facecolor("none")
ax13.set_facecolor("none")