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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: DecisionTreeClassifier")
st.write("Stroke: RandomForestClassifier")
st.write("Diabetes: XGBClassifier")
st.write("Heart Attack: RandomForestClassifier")
#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
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