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heart.csv

@@ -0,0 +1,1191 @@
+age,sex,chest pain type,resting bp s,cholesterol,fasting blood sugar,resting ecg,max heart rate,exercise angina,oldpeak,ST slope,target
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+67,1,3,152,212,0,2,150,0,0.8,2,1
+55,0,2,132,342,0,0,166,0,1.2,1,0
+44,1,4,120,169,0,0,144,1,2.8,3,1
+63,1,4,140,187,0,2,144,1,4,1,1
+63,0,4,124,197,0,0,136,1,0,2,1
+41,1,2,120,157,0,0,182,0,0,1,0
+59,1,4,164,176,1,2,90,0,1,2,1
+57,0,4,140,241,0,0,123,1,0.2,2,1
+45,1,1,110,264,0,0,132,0,1.2,2,1
+68,1,4,144,193,1,0,141,0,3.4,2,1
+57,1,4,130,131,0,0,115,1,1.2,2,1
+57,0,2,130,236,0,2,174,0,0,2,1
+38,1,3,138,175,0,0,173,0,0,1,0

main.py

@@ -0,0 +1,284 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.ensemble import RandomForestRegressor, VotingRegressor
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.linear_model import LinearRegression
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.svm import SVR
+from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
+from sklearn.neural_network import MLPRegressor
+from lightgbm import LGBMRegressor
+from xgboost import XGBRegressor
+import math
+
+st.title('Heart Disease Prediction Application')
+st.write('''
+         Please fill in the attributes below, then hit the Predict button
+         to get your results. 
+         ''')
+
+st.header('Input Attributes')
+age = st.slider('Your Age (Years)', min_value=0.0, max_value=100.0, value=50.0, step=1.0)
+st.write(''' ''')
+gen = st.radio("Your Gender", ('Male', 'Female'))
+st.write(''' ''')
+cp = st.radio("Chest Pain", ('Typical Angina', 'Atypical Angina', 'Non-Anginal Pain', 'Asymptomatic'))
+st.write(''' ''')
+resting_bp = st.slider('Resting Blood Pressure (In mm Hg)', min_value=0.0, max_value=200.0, value=100.0, step=1.0)
+st.write(''' ''')
+serum = st.slider('Serum Cholesterol (In mm mg/dl)', min_value=0.0, max_value=400.0, value=200.0, step=1.0)
+st.write(''' ''')
+bs = st.radio("Is Your Fasting Blood Sugar > 120 mg/dl?", ('Yes', 'No'))
+st.write(''' ''')
+re = st.radio("Resting Electrocardiogram Results", ('Normal', 'ST-T Wave Abnormality (T Wave Inversions and/or ST Elevation or Depression of > 0.05 mV)', 'Showing Probable or Definite Left Ventricular Hypertrophy by Estes Criteria'))
+st.write(''' ''')
+max_heart = st.slider('Maximum Heart Rate', min_value=0.0, max_value=300.0, value=150.0, step=1.0)
+st.write(''' ''')
+
+ex = st.radio("Exercise Induced Angina", ('Yes', 'No'))
+st.write(''' ''')
+oldpeak = st.slider('ST Depression Induced by Exercise Relative to Rest', min_value=-5.0, max_value=5.0, value=0.0, step=0.01)
+st.write(''' ''')
+sp = st.radio("The Slope of the Peak Exercise ST Segment", ('Upsloping', 'Flat', 'Downsloping'))
+st.write(''' ''')
+
+selected_models = st.multiselect("Choose Regressor Models", ('Random Forest',
+    'Linear Regression',
+    'K-Nearest Neighbors',
+    'Decision Tree',
+    'Support Vector Machine',
+    'Gradient Boosting Regression',
+    'XGBoost Regression',
+    'LightGBM Regression'))
+st.write(''' ''')
+
+# Initialize an empty list to store the selected models
+models_to_run = []
+
+# Check which models were selected and add them to the models_to_run list
+if 'Random Forest' in selected_models:
+    models_to_run.append(RandomForestRegressor())
+
+if 'Linear Regression' in selected_models:
+    models_to_run.append(LinearRegression())
+
+if 'K-Nearest Neighbors' in selected_models:
+    models_to_run.append(KNeighborsRegressor())
+
+if 'Decision Tree' in selected_models:
+    models_to_run.append(DecisionTreeRegressor())
+
+if 'Support Vector Machine' in selected_models:
+    models_to_run.append(SVR())
+
+if 'Gradient Boosting Regression' in selected_models:
+    models_to_run.append(GradientBoostingRegressor())
+
+if 'XGBoost Regression' in selected_models:
+    models_to_run.append(XGBRegressor())
+
+if 'LightGBM Regression' in selected_models:
+    models_to_run.append(LGBMRegressor())
+
+if 'Neural Network (MLP) Regression' in selected_models:
+    models_to_run.append(MLPRegressor())
+
+# gender conversion
+if gen == "Male":
+    gender = 1
+else:
+    gender = 0
+
+# Chest Pain
+if cp == "Typical Angina":
+    chest = 1
+elif cp == "Atypical Angina":
+    chest = 2
+elif cp == "Non-Anginal Pain":
+    chest = 3
+else:
+    chest = 4
+
+# blood_sugar conversion
+if bs == "Yes":
+    blood_sugar = 1
+else:
+    blood_sugar = 0
+
+# electro conversion
+if re == "Normal":
+    electro = 0
+elif re == "ST-T Wave Abnormality (T Wave Inversions and/or ST Elevation or Depression of > 0.05 mV)":
+    electro = 1
+else:
+    electro = 2
+
+# exercise conversion
+if ex == "Yes":
+    exercise = 1
+else:
+    exercise = 0
+
+# slope conversion
+if sp == "Upsloping":
+    slope = 1
+elif sp == "Flat":
+    slope = 2
+else:
+    slope = 3
+
+user_input = np.array([age, gender, chest, blood_sugar, resting_bp, serum, electro, max_heart,
+                       exercise, oldpeak, slope]).reshape(1, -1)
+
+# import dataset
+def get_dataset():
+    data = pd.read_csv('heart.csv')
+
+    return data
+
+def generate_model_labels(model_names):
+    model_labels = []
+    for name in model_names:
+        words = name.split()
+        if len(words) > 1:
+            # Multiple words, use initials
+            label = "".join(word[0] for word in words)
+        else:
+            # Single word, take the first 3 letters
+            label = name[:3]
+        model_labels.append(label)
+    return model_labels
+
+if st.button('Submit'):
+    df = get_dataset()
+
+    # fix column names
+    df.columns = (["age", "sex", "chest pain type", "resting bp s", "cholesterol",
+                   "fasting blood sugar", "resting ecg", "max heart rate", "exercise angina",
+                   "oldpeak", "ST slope", "target"])
+
+    # Split the dataset into train and test
+    X = df.drop('target', axis=1)
+    y = df['target']
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Create two columns to divide the screen
+    left_column, right_column = st.columns(2)
+
+    # Left column content
+    with left_column:
+        # Create a VotingRegressor with the selected models
+        ensemble = VotingRegressor(
+            estimators=[('rf', RandomForestRegressor()), ('lr', LinearRegression()), ('dt', DecisionTreeRegressor())]
+        )
+
+        # Fit the voting regressor to the training data
+        ensemble.fit(X_train, y_train)
+
+        # Make predictions on the test set
+        ensemble_predictions = ensemble.predict(user_input)
+
+        # Evaluate the model's performance on the test set
+        ensemble_r2 = r2_score(y_test, ensemble.predict(X_test))
+        ensemble_mse = mean_squared_error(y_test, ensemble.predict(X_test))
+        ensemble_mae = mean_absolute_error(y_test, ensemble.predict(X_test))
+        ensemble_rmse = np.sqrt(ensemble_mse)
+
+        st.write(f'According to Ensemble Model, You have a predicted value of: {ensemble_predictions[0]:.2f}')
+        st.write('Ensemble Model R-squared (R2) Score:', ensemble_r2)
+        st.write('Ensemble Model Root Mean Squared Error (RMSE):', ensemble_rmse)
+        st.write('Ensemble Model Mean Squared Error (MSE):', ensemble_mse)
+        st.write('Ensemble Model Mean Absolute Error (MAE):', ensemble_mae)
+        st.write('------------------------------------------------------------------------------------------------------')
+
+    # Add padding between the columns
+    st.empty()
+
+    # Right column content
+    with right_column:
+        for model in models_to_run:
+            # Train the selected model
+            model.fit(X_train, y_train)
+
+            # Make predictions on the test set
+            model_predictions = model.predict(user_input)
+
+            # Evaluate the model's performance on the test set
+            model_r2 = r2_score(y_test, model.predict(X_test))
+            model_mse = mean_squared_error(y_test, model.predict(X_test))
+            model_mae = mean_absolute_error(y_test, model.predict(X_test))
+            model_rmse = np.sqrt(model_mse)
+
+            st.write(f'According to {type(model).__name__} Model, You have a predicted value of: {model_predictions[0]:.2f}')
+            st.write(f'{type(model).__name__} Model R-squared (R2) Score:', model_r2)
+            st.write(f'{type(model).__name__} Model Root Mean Squared Error (RMSE):', model_rmse)
+            st.write(f'{type(model).__name__} Model Mean Squared Error (MSE):', model_mse)
+            st.write(f'{type(model).__name__} Model Mean Absolute Error (MAE):', model_mae)
+            st.write('------------------------------------------------------------------------------------------------------')
+
+    # Initialize lists to store model names and their respective performance metrics
+    model_names = ['Ensemble']
+    r2_scores = [ensemble_r2]
+    rmses = [ensemble_rmse]
+    mses = [ensemble_mse]
+    maes = [ensemble_mae]
+
+    # Loop through the selected models to compute their performance metrics
+    for model in models_to_run:
+        model_names.append(type(model).__name__)
+        model.fit(X_train, y_train)
+        model_predictions = model.predict(X_test)
+        model_r2 = r2_score(y_test, model_predictions)
+        model_mse = mean_squared_error(y_test, model_predictions)
+        model_mae = mean_absolute_error(y_test, model_predictions)
+        model_rmse = np.sqrt(model_mse)
+        r2_scores.append(model_r2)
+        rmses.append(model_rmse)
+        mses.append(model_mse)
+        maes.append(model_mae)
+
+    # Create a DataFrame to store the performance metrics
+    metrics_df = pd.DataFrame({
+        'Model': model_names,
+        'R-squared (R2)': r2_scores,
+        'Root Mean Squared Error (RMSE)': rmses,
+        'Mean Squared Error (MSE)': mses,
+        'Mean Absolute Error (MAE)': maes
+    })
+
+    # Get the model labels
+    model_labels = generate_model_labels(metrics_df['Model'])
+
+    # Plot the comparison graphs
+    plt.figure(figsize=(12, 10))
+
+    # R-squared (R2) score comparison
+    plt.subplot(2, 2, 1)
+    plt.bar(model_labels, metrics_df['R-squared (R2)'], color='skyblue')
+    plt.title('R-squared (R2) Score Comparison')
+    plt.ylim(0, 1)
+
+    # Root Mean Squared Error (RMSE) comparison
+    plt.subplot(2, 2, 2)
+    plt.bar(model_labels, metrics_df['Root Mean Squared Error (RMSE)'], color='orange')
+    plt.title('Root Mean Squared Error (RMSE) Comparison')
+
+    # Mean Squared Error (MSE) comparison
+    plt.subplot(2, 2, 3)
+    plt.bar(model_labels, metrics_df['Mean Squared Error (MSE)'], color='green')
+    plt.title('Mean Squared Error (MSE) Comparison')
+
+    # Mean Absolute Error (MAE) comparison
+    plt.subplot(2, 2, 4)
+    plt.bar(model_labels, metrics_df['Mean Absolute Error (MAE)'], color='purple')
+    plt.title('Mean Absolute Error (MAE) Comparison')
+
+    # Adjust layout to prevent overlapping of titles
+    plt.tight_layout()
+
+    # Display the graphs in Streamlit
+    st.pyplot()

requirements.txt

@@ -0,0 +1,7 @@
+lightgbm==4.0.0
+matplotlib==3.7.2
+numpy==1.25.1
+pandas==2.0.3
+scikit_learn==1.3.0
+streamlit==1.25.0
+xgboost==1.7.6