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00b1e78 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 | from flask import Flask, render_template, request, flash, redirect, url_for
import numpy as np
import pickle
app = Flask(__name__)
app.secret_key = 'your_secret_key_here' # Replace with a strong secret key
def predict_disease(patient_data):
"""
Predicts disease risks based on patient data.
Loads pre-trained models and scalers, prepares features, and returns a risk dictionary.
"""
try:
# Load models
heart_model = pickle.load(open('heart_rf_model.pkl', 'rb'))
diabetes_model = pickle.load(open('diabetes_model.pkl', 'rb'))
cirrhosis_model = pickle.load(open('cirrhosis_model.pkl', 'rb'))
hep_c_model = pickle.load(open('hep_c_model.pkl', 'rb'))
# Load scalers
heart_scaler = pickle.load(open('heart_scaler.pkl', 'rb'))
diabetes_scaler = pickle.load(open('diabetes_scaler.pkl', 'rb'))
cirrhosis_scaler = pickle.load(open('cirrhosis_scaler.pkl', 'rb'))
hep_c_scaler = pickle.load(open('hep_c_scaler.pkl', 'rb'))
# Heart Disease Features
heart_features = np.array([[
patient_data.get('Age', 55),
patient_data.get('Sex', 1),
patient_data.get('cp', 0),
patient_data.get('BP', 130),
patient_data.get('Cholesterol', 200),
patient_data.get('FBS', 0),
patient_data.get('EKG', 0),
patient_data.get('MaxHR', 150),
patient_data.get('ExerciseAngina', 0),
patient_data.get('STdepression', 0.0),
patient_data.get('STslope', 0),
patient_data.get('Vessels', 0),
patient_data.get('Thallium', 2)
]])
# Diabetes Features – only scaling Age
diabetes_features = np.array([[
1 if patient_data.get('Polyuria', 0) == 1 else 0,
1 if patient_data.get('Polydipsia', 0) == 1 else 0,
patient_data.get('Age', 55), # Age will be scaled
1 if patient_data.get('Gender', 'Male') == 'Male' else 0,
1 if patient_data.get('partial_paresis', 0) == 1 else 0,
1 if patient_data.get('sudden_weight_loss', 0) == 1 else 0,
1 if patient_data.get('Irritability', 0) == 1 else 0,
1 if patient_data.get('delayed_healing', 0) == 1 else 0,
1 if patient_data.get('Alopecia', 0) == 1 else 0,
1 if patient_data.get('Itching', 0) == 1 else 0
]])
# Scale Age for diabetes
age_scaled = diabetes_scaler.transform([[patient_data.get('Age', 55)]])
diabetes_features[0, 2] = age_scaled[0, 0]
# Cirrhosis Features
cirrhosis_features = np.array([[
patient_data.get('Bilirubin', 1.2),
patient_data.get('Albumin', 3.8),
patient_data.get('Copper', 80),
patient_data.get('Alk_Phos', 70),
patient_data.get('SGOT', 40),
patient_data.get('Tryglicerides', 150),
patient_data.get('Platelets', 250),
patient_data.get('Prothrombin', 11),
patient_data.get('Stage', 1),
patient_data.get('Age', 55),
patient_data.get('Sex', 1),
patient_data.get('Ascites', 0),
patient_data.get('Hepatomegaly', 0),
patient_data.get('Spiders', 0),
patient_data.get('Edema', 0)
]])
# Hepatitis C Features
hep_c_features = np.array([[
patient_data.get('Age', 55),
patient_data.get('Sex', 1),
patient_data.get('ALB', 4.0),
patient_data.get('ALP', 70),
patient_data.get('ALT', 45),
patient_data.get('AST', 38),
patient_data.get('BIL', 0.8),
patient_data.get('CHE', 8000),
patient_data.get('CHOL', 180),
patient_data.get('CREA', 0.9),
patient_data.get('GGT', 30),
patient_data.get('PROT', 7.0)
]])
# Scale features
heart_scaled = heart_scaler.transform(heart_features)
cirrhosis_scaled = cirrhosis_scaler.transform(cirrhosis_features)
hep_c_scaled = hep_c_scaler.transform(hep_c_features)
# Get prediction probabilities
heart_prob = heart_model.predict_proba(heart_scaled)[:, 1][0]
diabetes_prob = diabetes_model.predict_proba(diabetes_features)[:, 1][0]
cirrhosis_prob = cirrhosis_model.predict_proba(cirrhosis_scaled)[:, 1][0]
hep_c_prob = hep_c_model.predict_proba(hep_c_scaled)[:, 1][0]
# Compute overall risk score
final_score = (
(0.30 * heart_prob) +
(0.25 * diabetes_prob) +
(0.25 * cirrhosis_prob) +
(0.20 * hep_c_prob)
)
return {
'Heart Disease': {'Risk': 'High' if heart_prob > 0.5 else 'Low', 'Probability': round(heart_prob, 3)},
'Diabetes': {'Risk': 'High' if diabetes_prob > 0.5 else 'Low', 'Probability': round(diabetes_prob, 3)},
'Cirrhosis': {'Risk': 'High' if cirrhosis_prob > 0.5 else 'Low', 'Probability': round(cirrhosis_prob, 3)},
'Hepatitis C': {'Risk': 'High' if hep_c_prob > 0.5 else 'Low', 'Probability': round(hep_c_prob, 3)},
'Overall Risk Score': round(final_score, 3)
}
except Exception as e:
raise Exception(f"Error in prediction: {str(e)}")
@app.route('/', methods=['GET', 'POST'])
def index():
result = None
if request.method == 'POST':
try:
# Collect input parameters from the form
patient_data = {
# General / Heart Disease
'Age': int(request.form.get('Age', 55)),
'Sex': int(request.form.get('Sex', 1)),
'cp': int(request.form.get('cp', 0)),
'BP': float(request.form.get('BP', 130)),
'Cholesterol': float(request.form.get('Cholesterol', 200)),
'FBS': int(request.form.get('FBS', 0)),
'EKG': int(request.form.get('EKG', 0)),
'MaxHR': int(request.form.get('MaxHR', 150)),
'ExerciseAngina': int(request.form.get('ExerciseAngina', 0)),
'STdepression': float(request.form.get('STdepression', 0.0)),
'STslope': int(request.form.get('STslope', 0)),
'Vessels': int(request.form.get('Vessels', 0)),
'Thallium': int(request.form.get('Thallium', 2)),
# Diabetes
'Polyuria': int(request.form.get('Polyuria', 0)),
'Polydipsia': int(request.form.get('Polydipsia', 0)),
'Gender': request.form.get('Gender', 'Male'),
'partial_paresis': int(request.form.get('partial_paresis', 0)),
'sudden_weight_loss': int(request.form.get('sudden_weight_loss', 0)),
'Irritability': int(request.form.get('Irritability', 0)),
'delayed_healing': int(request.form.get('delayed_healing', 0)),
'Alopecia': int(request.form.get('Alopecia', 0)),
'Itching': int(request.form.get('Itching', 0)),
# Cirrhosis
'Bilirubin': float(request.form.get('Bilirubin', 1.2)),
'Albumin': float(request.form.get('Albumin', 3.8)),
'Copper': float(request.form.get('Copper', 80)),
'Alk_Phos': float(request.form.get('Alk_Phos', 70)),
'SGOT': float(request.form.get('SGOT', 40)),
'Tryglicerides': float(request.form.get('Tryglicerides', 150)),
'Platelets': float(request.form.get('Platelets', 250)),
'Prothrombin': float(request.form.get('Prothrombin', 11)),
'Stage': int(request.form.get('Stage', 1)),
'Ascites': int(request.form.get('Ascites', 0)),
'Hepatomegaly': int(request.form.get('Hepatomegaly', 0)),
'Spiders': int(request.form.get('Spiders', 0)),
'Edema': int(request.form.get('Edema', 0)),
# Hepatitis C
'ALB': float(request.form.get('ALB', 4.0)),
'ALP': float(request.form.get('ALP', 70)),
'ALT': float(request.form.get('ALT', 45)),
'AST': float(request.form.get('AST', 38)),
'BIL': float(request.form.get('BIL_hep', 0.8)), # To distinguish from cirrhosis bilirubin
'CHE': float(request.form.get('CHE', 8000)),
'CHOL': float(request.form.get('CHOL_hep', 180)), # To distinguish from heart cholesterol
'CREA': float(request.form.get('CREA', 0.9)),
'GGT': float(request.form.get('GGT', 30)),
'PROT': float(request.form.get('PROT_hep', 7.0))
}
# Get the prediction result
result = predict_disease(patient_data)
except Exception as e:
flash(str(e))
return render_template('index.html', result=result)
if __name__ == '__main__':
app.run(debug=True, port=1234)
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