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| from pycaret.classification import * | |
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| model = load_model('lr') | |
| def predict(model, input_df): | |
| predictions_df = predict_model(estimator=model, raw_score=True, data=input_df) | |
| predictions = predictions_df['prediction_score_1'][0] | |
| return predictions | |
| def run(): | |
| from PIL import Image | |
| image_hospital = Image.open('tehran.jpg') | |
| st.sidebar.info('This app is created by Sepehr Nayebirad to predict reoperation due to bleeding after CABG surgery') | |
| st.sidebar.image(image_hospital) | |
| st.title("Reoperation Prediction App") | |
| Age = st.number_input('Age', min_value=1, max_value=100, value=25) | |
| Hgb = st.number_input('Hgb', min_value=1.00, max_value=50.00, value=13.00,step=1.,format="%.2f") | |
| Cr = st.number_input('Cr', min_value=0.01, max_value=50.00, value=1.00,step=1.,format="%.2f") | |
| Gender = st.selectbox('Gender', ['male', 'female']) | |
| if Gender == 'male': | |
| Gender=1 | |
| else: | |
| Gender=0 | |
| if st.checkbox('Diabetes'): | |
| Diabetes = 1 | |
| else: | |
| Diabetes = 0 | |
| if st.checkbox('Positive family history'): | |
| Positive_family_history = 1 | |
| else: | |
| Positive_family_history = 0 | |
| if st.checkbox('Dyslipidemia'): | |
| Dyslipidemia = 1 | |
| else: | |
| Dyslipidemia = 0 | |
| if st.checkbox('Current cigarette smoking'): | |
| smoker = 1 | |
| else: | |
| smoker = 0 | |
| if st.checkbox('Current opium consumption'): | |
| opium = 1 | |
| else: | |
| opium = 0 | |
| if st.checkbox('Hx of Angioplasty POBA'): | |
| poba = 1 | |
| else: | |
| poba = 0 | |
| if st.checkbox('Hx of renal failure'): | |
| renal_failure = 1 | |
| else: | |
| renal_failure = 0 | |
| if st.checkbox('Previous antiplatelet use'): | |
| antiplatelet = 1 | |
| else: | |
| antiplatelet = 0 | |
| if st.checkbox('LM involvement'): | |
| lm = 1 | |
| else: | |
| lm = 0 | |
| if st.checkbox('Urgent/emergent surgery'): | |
| emergent = 1 | |
| else: | |
| emergent = 0 | |
| if st.checkbox('Off-pump surgery'): | |
| offpump = 1 | |
| else: | |
| offpump = 0 | |
| if st.checkbox('Cabg+valve surgery'): | |
| valve = 1 | |
| else: | |
| valve = 0 | |
| mi = st.selectbox('time of MI', ['MI <24h', '1d < MI < 7d', 'other']) | |
| if mi == 'MI <24h': | |
| mi24, mi1_7 = 1, 0 | |
| elif mi == '1d < MI < 7d': | |
| mi24, mi1_7 = 0, 1 | |
| elif mi == 'other': | |
| mi24, mi1_7 = 0, 0 | |
| output="" | |
| input_dict = {'Gender' : Gender, 'Diabetes':Diabetes, 'Positive family history':Positive_family_history, | |
| 'Dyslipidemia' : Dyslipidemia,'Current cigarette smoking' : smoker,'Current opium consumption' : opium, | |
| 'Hx of Angioplasty POBA' : poba, 'Hx of renal failure' : renal_failure, | |
| 'Dyslipidemia' : Dyslipidemia, 'MI <24h': mi24, '1d < MI < 7d': mi1_7, | |
| 'Previous antiplatelet use' : antiplatelet, 'LM involvement':lm, | |
| 'Age': Age, 'Hgb': Hgb, 'Cr':Cr, 'Urgent/emergent surgery':emergent, | |
| 'Off-pump surgery': offpump, 'Cabg+valve surgery': valve, | |
| } | |
| input_df = pd.DataFrame([input_dict]) | |
| if st.button("Predict"): | |
| output = predict(model=model, input_df=input_df) | |
| output = 100*output | |
| st.success('Reoperation risk due to bleeding is {}'.format(output)+'%') | |
| run() | |