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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import joblib | |
| from sklearn.ensemble import RandomForestClassifier, VotingClassifier | |
| from sklearn.tree import DecisionTreeClassifier | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.neighbors import KNeighborsClassifier | |
| from sklearn.svm import SVC | |
| from sklearn.naive_bayes import GaussianNB | |
| from sklearn.neural_network import MLPClassifier | |
| from sklearn.ensemble import GradientBoostingClassifier | |
| from xgboost import XGBClassifier | |
| from lightgbm import LGBMClassifier | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score | |
| st.title('Disease Prediction Application') | |
| st.write(''' | |
| Please fill in the attributes below, then hit the Predict button | |
| to get your results. | |
| ''') | |
| st.header('General Information') | |
| gen = st.radio("Your Gender", ('Male', 'Female')) | |
| st.write(''' ''') | |
| age = st.slider('Your Age (Years)', min_value=0.0, max_value=100.0, value=50.0, step=1.0) | |
| st.write(''' ''') | |
| bmi = st.slider('BMI', min_value=0.0, max_value=50.0, value=25.0, step=0.1) | |
| st.write(''' ''') | |
| st.header('Blood Test Information') | |
| bs = st.radio("Is Your Fasting Blood Sugar > 120 mg/dl?", ('Yes', 'No')) | |
| st.write(''' ''') | |
| urea = st.slider('Urea', min_value=0.0, max_value=100.0, value=50.0, step=0.1) | |
| st.write(''' ''') | |
| cr = st.slider('Creatinine Ratio(Cr)', min_value=0.0, max_value=1000.0, value=500.0, step=1.0) | |
| st.write(''' ''') | |
| hb = st.slider('HbA1c', min_value=0.0, max_value=20.0, value=10.0, step=0.1) | |
| st.write(''' ''') | |
| st.header('Cholesterol Test Information') | |
| chol = st.slider('Cholesterol (Chol)', min_value=0.0, max_value=20.0, value=10.0, step=0.1) | |
| st.write(''' ''') | |
| tg = st.slider('Triglycerides(TG) Cholesterol', min_value=0.0, max_value=20.0, value=10.0, step=0.1) | |
| st.write(''' ''') | |
| hdl = st.slider('HDL Cholesterol', min_value=0.0, max_value=20.0, value=10.0, step=0.1) | |
| st.write(''' ''') | |
| ldl = st.slider('LDL Cholesterol', min_value=0.0, max_value=20.0, value=10.0, step=0.1) | |
| st.write(''' ''') | |
| vldl = st.slider('VLDL Cholesterol', min_value=0.0, max_value=50.0, value=25.0, step=0.1) | |
| st.write(''' ''') | |
| st.header('Cardio Test Information') | |
| cp = st.radio("Chest Pain", ('Typical Angina', 'Atypical Angina', 'Non-Anginal Pain', 'Asymptomatic')) | |
| 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(''' ''') | |
| 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(''' ''') | |
| 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(''' ''') | |
| 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(''' ''') | |
| # 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, electro, max_heart, | |
| exercise, oldpeak, slope, urea, cr, hb, chol, tg, hdl, ldl, vldl, bmi]).reshape(1, -1) | |
| # import dataset | |
| def get_dataset(): | |
| data = pd.read_csv('Fianl Dataset.csv') | |
| # Calculate the correlation matrix | |
| # corr_matrix = data.corr() | |
| # Create a heatmap of the correlation matrix | |
| # plt.figure(figsize=(10, 8)) | |
| # sns.heatmap(corr_matrix, annot=True, cmap='coolwarm') | |
| # plt.title('Correlation Matrix') | |
| # plt.xticks(rotation=45) | |
| # plt.yticks(rotation=0) | |
| # plt.tight_layout() | |
| # Display the heatmap in Streamlit | |
| # st.pyplot() | |
| return data | |
| if st.button('Submit'): | |
| # Load your dataset for prediction | |
| df = get_dataset() | |
| # Split the dataset into features and targets for Heart and Diabetes prediction | |
| X = df.drop(['Diabetes', 'Heart'], axis=1) | |
| y_heart = df['Heart'] | |
| y_diabetes = df['Diabetes'] | |
| # Create an ensemble model for Heart prediction | |
| random_forest_model_heart = RandomForestClassifier(random_state=42) | |
| naive_bayes_model_heart = GaussianNB() | |
| gradient_boosting_model_heart = GradientBoostingClassifier(random_state=42) | |
| # Create a voting ensemble with soft voting for Heart prediction | |
| ensemble_model_heart = VotingClassifier(estimators=[ | |
| ('Random Forest', random_forest_model_heart), | |
| ('Naive Bayes', naive_bayes_model_heart), | |
| ('Gradient Boosting', gradient_boosting_model_heart) | |
| ], voting='soft') | |
| # Create an ensemble model for Diabetes prediction | |
| random_forest_model_diabetes = RandomForestClassifier(random_state=42) | |
| naive_bayes_model_diabetes = GaussianNB() | |
| gradient_boosting_model_diabetes = GradientBoostingClassifier(random_state=42) | |
| # Create a voting ensemble with soft voting for Diabetes prediction | |
| ensemble_model_diabetes = VotingClassifier(estimators=[ | |
| ('Random Forest', random_forest_model_diabetes), | |
| ('Naive Bayes', naive_bayes_model_diabetes), | |
| ('Gradient Boosting', gradient_boosting_model_diabetes) | |
| ], voting='soft') | |
| # Split the data into training and testing sets for both targets | |
| X_train, X_test, y_heart_train, y_heart_test, y_diabetes_train, y_diabetes_test = train_test_split( | |
| X, y_heart, y_diabetes, test_size=0.2, random_state=42 | |
| ) | |
| # Ensure the user input has the correct number of features for Heart prediction | |
| if user_input.shape[1] == X_train.shape[1]: | |
| # Fit the ensemble model for Heart prediction on the training data | |
| ensemble_model_heart.fit(X_train, y_heart_train) | |
| # Make predictions on user input for Heart | |
| prediction_heart = ensemble_model_heart.predict(user_input) | |
| prediction_proba_heart = ensemble_model_heart.predict_proba(user_input) | |
| # Check if the dimensions of prediction_heart match y_heart_test | |
| if prediction_heart.shape[0] == y_heart_test.shape[0]: | |
| # You can add a threshold and provide a prediction based on class 1 for Heart | |
| threshold_heart = 0.5 | |
| if prediction_proba_heart[0][1] >= threshold_heart: | |
| st.header("Predicted Heart Disease: You might have Heart Disease") | |
| st.write("Predicted Probability of Having Heart Disease:", prediction_proba_heart[0][1] * 100) | |
| else: | |
| st.header("Predicted Heart Disease: You do not have Heart Disease") | |
| st.write("Predicted Probability of Having Heart Disease:", prediction_proba_heart[0][1] * 100) | |
| # Calculate and print Heart prediction metrics | |
| accuracy_heart = accuracy_score(y_heart_test, prediction_heart) | |
| precision_heart = precision_score(y_heart_test, prediction_heart) | |
| recall_heart = recall_score(y_heart_test, prediction_heart) | |
| f1_heart = f1_score(y_heart_test, prediction_heart) | |
| st.write("Heart Prediction Metrics:") | |
| st.write("Accuracy:", accuracy_heart) | |
| st.write("Precision:", precision_heart) | |
| st.write("Recall:", recall_heart) | |
| st.write("F1-score:", f1_heart) | |
| st.write("____________________________________________________________________________________________") | |
| else: | |
| st.write("Error: Inconsistent dimensions in Heart prediction. Please check your data.") | |
| else: | |
| st.write("Error: Input features do not match the dataset. Please provide valid input.") | |
| # Ensure the user input has the correct number of features for Diabetes prediction | |
| if user_input.shape[1] == X_train.shape[1]: | |
| # Fit the ensemble model for Diabetes prediction on the training data | |
| ensemble_model_diabetes.fit(X_train, y_diabetes_train) | |
| # Make predictions on user input for Diabetes | |
| pred_diabetes = ensemble_model_diabetes.predict(user_input) | |
| pred_diabetes_proba = ensemble_model_diabetes.predict_proba(user_input) | |
| # Check if the dimensions of pred_diabetes match y_diabetes_test | |
| if pred_diabetes.shape[0] == y_diabetes_test.shape[0]: | |
| # You can add a threshold and provide a prediction based on class 1 for Diabetes | |
| threshold_diabetes = 0.5 | |
| if pred_diabetes_proba[0][1] >= threshold_diabetes: | |
| st.header("Predicted Diabetes: You might have Diabetes") | |
| st.write("Predicted Probability of Having Diabetes:", pred_diabetes_proba[0][1] * 100) | |
| else: | |
| st.header("Predicted Diabetes: You do not have Diabetes") | |
| st.write("Predicted Probability of Having Diabetes:", pred_diabetes_proba[0][1] * 100) | |
| # Calculate and print Diabetes prediction metrics | |
| accuracy_diabetes = accuracy_score(y_diabetes_test, pred_diabetes) | |
| precision_diabetes = precision_score(y_diabetes_test, pred_diabetes) | |
| recall_diabetes = recall_score(y_diabetes_test, pred_diabetes) | |
| f1_diabetes = f1_score(y_diabetes_test, pred_diabetes) | |
| st.write("Diabetes Prediction Metrics:") | |
| st.write("Accuracy:", accuracy_diabetes) | |
| st.write("Precision:", precision_diabetes) | |
| st.write("Recall:", recall_diabetes) | |
| st.write("F1-score:", f1_diabetes) | |
| else: | |
| st.write("Error: Inconsistent dimensions in Diabetes prediction. Please check your data.") | |
| else: | |
| st.write("Error: Input features do not match the dataset. Please provide valid input.") | |