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| import streamlit as st | |
| import pandas as pd | |
| import altair as alt | |
| import numpy as np | |
| from PIL import Image | |
| import pickle | |
| with open('best_model_hypertension.pkl', 'rb') as file: | |
| hypertension_model = pickle.load(file) | |
| with open('hypertension_scaler.pkl', 'rb') as file: | |
| hypertension_scaler = pickle.load(file) | |
| with open('best_model_stroke.pkl', 'rb') as file: | |
| stroke_model = pickle.load(file) | |
| with open('stroke_scaler.pkl', 'rb') as file: | |
| stroke_scaler = pickle.load(file) | |
| with open('best_model_diabetes.pkl', 'rb') as file: | |
| diabetes_model = pickle.load(file) | |
| with open('diabetes_scaler.pkl', 'rb') as file: | |
| diabetes_scaler = pickle.load(file) | |
| with open('best_model_heart.pkl', 'rb') as file: | |
| heart_model = pickle.load(file) | |
| with open('best_scaler_heart.pkl', 'rb') as file: | |
| heart_scaler = pickle.load(file) | |
| #Create header | |
| st.write("""# Heart Attack Risk Predictor""") | |
| st.write("""## How it works""") | |
| st.write("view your predictions about your health condition based on your answers to the questions on the side panel.") | |
| #image | |
| st.write("""## Training Flow Diagram""") | |
| image = Image.open('Train_diag.png') | |
| st.image(image) | |
| st.write("""## Prediction Flow Diagram""") | |
| image = Image.open('Test_diag_v2.png') | |
| st.image(image) | |
| #links | |
| st.write("""## Dataset links""") | |
| st.write("https://www.kaggle.com/datasets/prosperchuks/health-dataset?select=diabetes_data.csv") | |
| st.write("https://www.kaggle.com/datasets/iamsouravbanerjee/heart-attack-prediction-dataset") | |
| # model types | |
| st.write("""## Trained Model Types""") | |
| st.write("Hypertension: ") | |
| st.write("Stroke: ") | |
| st.write("Diabetes: ") | |
| st.write("Heart Attack: ") | |
| #Bring in the data | |
| data = pd.read_csv('heart_attack_prediction_dataset.csv') | |
| st.write("## HEART ATTACK TRAIN DATA") | |
| st.dataframe(data) | |
| #Create and name sidebar | |
| st.sidebar.header('Fill your survey') | |
| st.sidebar.write("""#### Choose your values""") | |
| def user_input_features(): | |
| age = st.sidebar.slider('Age', 18, 100, 25, 1) | |
| sex = st.sidebar.slider('Sex (Male : 0, Female : 1)', 0, 1, 0, 1) | |
| gen_health = st.sidebar.slider('General Health scale 1 = excellent ,2 = very good, 3 = good, 4 = fair, 5 = poor', 1, 5, 3, 1) | |
| men_health = st.sidebar.slider('days of poor mental health scale 1-30 days', 0, 30, 0, 1) | |
| cholesterol = st.sidebar.slider('Cholesterol level', 0, 600, 150, 5) | |
| heart_rate = st.sidebar.slider('Heart Rate', 0, 160, 60, 1) | |
| family_history = st.sidebar.slider('Family History(for heart attack). 0 = no,1 = yes', 0, 1, 0, 1) | |
| obesity = st.sidebar.slider('Obesity. 0 = no,1 = yes', 0, 1, 0, 1) | |
| alcohol_consumption = st.sidebar.slider('Alcohol Consumption(regularly).0 = no,1 = yes', 0, 1, 0, 1) | |
| smoking_status = st.sidebar.slider('Smoking(regularly).0 = no,1 = yes', 0, 1, 0, 1) | |
| exercise_hours = st.sidebar.slider('Exercise Hours Per Week', 0, 50, 15, 1) | |
| stress_level = st.sidebar.slider('Stress Level', 0, 10, 3, 1) | |
| sedentary_hours = st.sidebar.slider('Sedentary Hours Per Day', 0.0, 12.0, 6.0, 0.5) | |
| income = st.sidebar.slider('Income', 0, 500000, 0, 1000) | |
| education_level = st.sidebar.slider('Education level 1-10', 1, 10, 6, 1) | |
| bmi = st.sidebar.slider('BMI', 0.0, 50.0, 20.0, 0.1) | |
| triglycerides = st.sidebar.slider('Triglycerides Level', 0, 1000, 350, 10) | |
| physical_days = st.sidebar.slider('Physical Activity Days Per Week', 0, 7, 3, 1) | |
| sleep_hours = st.sidebar.slider('Sleep Hours Per Day', 0.0, 16.0, 8.0, 0.5) | |
| systolic = st.sidebar.slider('Blood Pressure (Systolic)', 0, 200, 140, 1) | |
| diastolic = st.sidebar.slider('Blood Pressure (Diastolic)', 0, 120, 80, 1) | |
| diff_walk = st.sidebar.slider('Do you have serious difficulty walking or climbing stairs? 0 = no 1 = yes', 0, 1, 0, 1) | |
| fruits = st.sidebar.slider('Consume Fruit 1 or more times per day. 0 = no,1 = yes', 0, 1, 0, 1) | |
| veggies = st.sidebar.slider('Consume Vegetables 1 or more times per day. 0 = no ,1 = yes', 0, 1, 0, 1) | |
| married = st.sidebar.slider('Ever Married. 0 = no,1 = yes', 0, 1, 0, 1) | |
| work_type = st.sidebar.slider('patient job type: 0 - Never_worked, 1 - children, 2 - Govt_job, 3 - Self-employed, 4 - Private', 0, 4, 0, 1) | |
| avg_glucose_level = st.sidebar.slider('Avg. glucose level', 0, 300, 100, 5) | |
| cp = st.sidebar.slider('Chest pain type: 0: asymptomatic 1: typical angina 2: atypical angina 3: non-anginal pain', 0, 3, 0, 1) | |
| trestbps = st.sidebar.slider('Resting blood pressure', 50, 250, 120, 1) | |
| thalach = st.sidebar.slider('Maximum heart rate achieved', 50, 250, 120, 1) | |
| exang = st.sidebar.slider('Exercise induced angina. 0 = no,1 = yes', 0, 1, 0, 1) | |
| oldpeak = st.sidebar.slider('ST depression induced by exercise relative to rest.', 0.0, 10.0, 0.0, 0.1) | |
| slope = st.sidebar.slider('The slope of the peak exercise ST segment: 0: upsloping 1: flat 2: downsloping', 0, 2, 2, 1) | |
| ca = st.sidebar.slider('Number of major vessels (0–3) colored by flourosopy', 0, 5, 0, 1) | |
| thal = st.sidebar.slider('3: Normal; 6: Fixed defect; 7: Reversable defect', 0, 10, 2, 1) | |
| user_data_hypertension = { | |
| 'cp' : cp, | |
| 'trestbps' : trestbps, | |
| 'chol' : cholesterol, | |
| 'thalach' : thalach, | |
| 'exang' : exang, | |
| 'oldpeak' : oldpeak, | |
| 'slope' : slope, | |
| 'ca' : ca, | |
| 'thal' : thal, | |
| } | |
| features_hypertension = pd.DataFrame(user_data_hypertension, index=[0]) | |
| features_hypertension_scaled = hypertension_scaler.transform(features_hypertension) | |
| pred_hypertension = hypertension_model.predict(features_hypertension_scaled) | |
| user_data_stroke = { | |
| 'age' : age, | |
| 'hypertension' : pred_hypertension[0], | |
| 'heart_disease' : 0, | |
| 'ever_married' : married, | |
| 'work_type' : work_type, | |
| 'avg_glucose_level' : avg_glucose_level, | |
| 'bmi' : bmi, | |
| 'smoking_status' : smoking_status | |
| } | |
| features_stroke = pd.DataFrame(user_data_stroke, index=[0]) | |
| features_stroke_scaled = stroke_scaler.transform(features_stroke) | |
| pred_stroke = stroke_model.predict(features_stroke_scaled) | |
| if physical_days > 2: | |
| PhysHlth = 1 | |
| else: | |
| PhysHlth = 0 | |
| if exercise_hours > 8: | |
| PhysActivity = 1 | |
| else: | |
| PhysActivity = 0 | |
| age_level = ((age -18) // 5 ) + 1 | |
| income_level = (income // 50000 ) + 1 | |
| user_data_diabetes = { | |
| 'HighBP': pred_hypertension[0], | |
| 'BMI': bmi, | |
| 'Stroke': pred_stroke[0], | |
| 'PhysActivity': PhysActivity, | |
| 'Fruits': fruits, | |
| 'Veggies': veggies, | |
| 'HvyAlcoholConsump': alcohol_consumption, | |
| 'GenHlth': gen_health, | |
| 'MentHlth': men_health, | |
| 'PhysHlth': PhysHlth, | |
| 'DiffWalk': diff_walk, | |
| 'Sex': 1 - sex, | |
| 'Age': age_level, | |
| 'Education': education_level, | |
| 'Income': income_level | |
| } | |
| features_diabetes = pd.DataFrame(user_data_diabetes, index=[0]) | |
| features_diabetes_scaled = diabetes_scaler.transform(features_diabetes) | |
| pred_diabetes = diabetes_model.predict(features_diabetes_scaled) | |
| user_data_heart_attack ={ | |
| 'Age': age, | |
| 'Cholesterol': cholesterol, | |
| 'Heart Rate': heart_rate, | |
| 'Diabetes': pred_diabetes[0], | |
| 'Family History': family_history, | |
| 'Obesity': obesity, | |
| 'Alcohol Consumption': alcohol_consumption, | |
| 'Exercise Hours Per Week' : exercise_hours, | |
| 'Stress Level': stress_level, | |
| 'Sedentary Hours Per Day': sedentary_hours, | |
| 'Income': income, | |
| 'BMI': bmi, | |
| 'Triglycerides': triglycerides, | |
| 'Physical Activity Days Per Week': physical_days, | |
| 'Sleep Hours Per Day': sleep_hours, | |
| 'BP_Systolic': systolic, | |
| 'BP_Diastolic': diastolic, | |
| 'Sex_Female': sex, | |
| 'Sex_Male': 1 - sex, | |
| } | |
| features_heart_attack = pd.DataFrame(user_data_heart_attack, index=[0]) | |
| features_heart_attack_scaled = heart_scaler.transform(features_heart_attack) | |
| pred_heart = heart_model.predict(features_heart_attack_scaled) | |
| return features_stroke,pred_stroke, features_hypertension, pred_hypertension, features_diabetes, pred_diabetes, features_heart_attack, pred_heart | |
| df_stroke, pred_stroke,df_hypertension, pred_hypertension,df_diabetes, pred_diabetes, df_heart_attack, pred_heart = user_input_features() | |
| st.write("## YOUR PREDICTIONS: ") | |
| st.write("## Hypertension User Input: ") | |
| df_hypertension | |
| st.write("Predicted Hypertension: ") | |
| pred_hypertension | |
| st.write("## Stroke User Input and Hypertension(pred. vals added): ") | |
| df_stroke | |
| st.write("Predicted Stroke: ") | |
| pred_stroke | |
| st.write("## Diabetes User Input and Hypertension and Stroke(pred. vals added): ") | |
| df_diabetes | |
| st.write("Predicted Diabetes: ") | |
| pred_diabetes | |
| st.write("## Heart Attack User Input and Diabetes: ") | |
| df_heart_attack | |
| st.write("Predicted Heart Attack: ") | |
| pred_heart | |