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import streamlit as st |
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import numpy as np |
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import joblib as jl |
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import xgboost as xgb |
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import pickle |
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with open('xgb_classifier_model_heart.pkl', 'rb') as model_file: |
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loaded_xgb_model = pickle.load(model_file) |
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st.title("Heart Attack Risk Prediction") |
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st.markdown("Enter the following information to predict your risk of a Heart Attack.") |
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sex = st.selectbox("Sex", ["Male", "Female"]) |
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chest_pain = st.selectbox("Chest Pain", ["Typical Angina", "Atypical Angina", "Non-Anginal pain", "Asymptomatic"]) |
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Max_heartrate_achieved = st.number_input("Maximum Heartrate Achieved ") |
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Exercice_angina = st.selectbox("Exercise Induced Angina", ["Yes", "No"]) |
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oldpeak = st.number_input("oldpeak") |
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slop = st.selectbox("Slope of the peak exercise ST segment", ["Unsloping", "flat", "Downsloping"]) |
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nb_vessels = st.selectbox("Number of major vessels", ["0", "1", "2", "3"]) |
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thal = st.selectbox("Thalassemia", ["Null", "Fixed defect", "Normal", "Reversible defect"]) |
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if st.button("Predict"): |
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sex = 1 if sex == "Male" else 0 |
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chest_pain = 0 if chest_pain == "Typical Angina" else 1 if chest_pain == "Atypical Angina" else 2 if chest_pain == "Non-Anginal pain" else 3 |
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Exercice_angina = 1 if Exercice_angina == "Yes" else 0 |
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slop = 0 if slop == "Unsloping" else 1 if slop == "flat" else 2 if slop == "Downsloping" else 0 |
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nb_vessels = 0 if nb_vessels == "0" else 1 if nb_vessels == "1" else 2 if nb_vessels == "2" else 3 |
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thal = 0 if thal == "Null" else 1 if thal == "Fixed defect" else 2 if thal == "Normal" else 3 if thal == "Reversible defect" else 0 |
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input_data = np.array([[sex, chest_pain, Max_heartrate_achieved, Exercice_angina,oldpeak, slop, nb_vessels, thal]]) |
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prediction = loaded_xgb_model.predict(input_data) |
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if prediction == 0: |
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st.markdown("### Result: **Low Risk** of a Heart Attack") |
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else: |
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st.markdown("### Result: **High Risk** of a Heart Attack") |
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