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"""
PRESAGE Model Training Pipeline
Trains 3 disease risk prediction models from provided datasets
with cross-validation and accuracy optimization
"""
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
import joblib
import warnings
warnings.filterwarnings('ignore')
print("=" * 70)
print("PRESAGE MODEL TRAINING PIPELINE")
print("=" * 70)
# ============================================================================
# MODEL 1: DIABETES RISK PREDICTION
# ============================================================================
print("\n[1/3] TRAINING DIABETES RISK MODEL...")
print("-" * 70)
try:
df_diabetes = pd.read_csv('diabetes_data.csv')
print(f"✓ Loaded diabetes dataset: {df_diabetes.shape}")
# Prepare features and target
X_diabetes = df_diabetes.drop('Diabetes', axis=1)
y_diabetes = df_diabetes['Diabetes']
# Split data
X_train, X_test, y_train, y_test = train_test_split(
X_diabetes, y_diabetes, test_size=0.2, random_state=42, stratify=y_diabetes
)
# Scale features
scaler_diabetes = StandardScaler()
X_train_scaled = scaler_diabetes.fit_transform(X_train)
X_test_scaled = scaler_diabetes.transform(X_test)
# Train multiple models and select best
models_diabetes = {
'Logistic Regression': LogisticRegression(max_iter=1000, random_state=42),
'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1),
'Gradient Boosting': GradientBoostingClassifier(n_estimators=100, random_state=42)
}
best_model_diabetes = None
best_score_diabetes = 0
results_diabetes = {}
for name, model in models_diabetes.items():
model.fit(X_train_scaled, y_train)
y_pred = model.predict(X_test_scaled)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, zero_division=0)
recall = recall_score(y_test, y_pred, zero_division=0)
f1 = f1_score(y_test, y_pred, zero_division=0)
results_diabetes[name] = {
'accuracy': accuracy,
'precision': precision,
'recall': recall,
'f1': f1
}
print(f"\n {name}:")
print(f" Accuracy: {accuracy:.4f}")
print(f" Precision: {precision:.4f}")
print(f" Recall: {recall:.4f}")
print(f" F1-Score: {f1:.4f}")
if accuracy > best_score_diabetes:
best_score_diabetes = accuracy
best_model_diabetes = model
# Save best diabetes model
diabetes_model_path = 'data/risk_models/diabetes_model.pkl'
joblib.dump(best_model_diabetes, diabetes_model_path)
print(f"\n✓ Best Diabetes Model Saved: {diabetes_model_path}")
print(f" Accuracy: {best_score_diabetes:.4f}")
except Exception as e:
print(f"✗ Error training diabetes model: {e}")
# ============================================================================
# MODEL 2: CARDIOVASCULAR RISK PREDICTION
# ============================================================================
print("\n[2/3] TRAINING CARDIOVASCULAR RISK MODEL...")
print("-" * 70)
try:
# Read and parse cardiovascular data (semicolon-delimited)
df_cardio = pd.read_csv('cardio_train.csv', sep=';')
print(f"✓ Loaded cardiovascular dataset: {df_cardio.shape}")
# Target is 'cardio' column
X_cardio = df_cardio.drop('cardio', axis=1)
y_cardio = df_cardio['cardio']
# Split data
X_train, X_test, y_train, y_test = train_test_split(
X_cardio, y_cardio, test_size=0.2, random_state=42, stratify=y_cardio
)
# Scale features
scaler_cardio = StandardScaler()
X_train_scaled = scaler_cardio.fit_transform(X_train)
X_test_scaled = scaler_cardio.transform(X_test)
# Train multiple models
models_cardio = {
'Logistic Regression': LogisticRegression(max_iter=1000, random_state=42),
'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1),
'Gradient Boosting': GradientBoostingClassifier(n_estimators=100, random_state=42)
}
best_model_cardio = None
best_score_cardio = 0
results_cardio = {}
for name, model in models_cardio.items():
model.fit(X_train_scaled, y_train)
y_pred = model.predict(X_test_scaled)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, zero_division=0)
recall = recall_score(y_test, y_pred, zero_division=0)
f1 = f1_score(y_test, y_pred, zero_division=0)
results_cardio[name] = {
'accuracy': accuracy,
'precision': precision,
'recall': recall,
'f1': f1
}
print(f"\n {name}:")
print(f" Accuracy: {accuracy:.4f}")
print(f" Precision: {precision:.4f}")
print(f" Recall: {recall:.4f}")
print(f" F1-Score: {f1:.4f}")
if accuracy > best_score_cardio:
best_score_cardio = accuracy
best_model_cardio = model
# Save best cardiovascular model
cardio_model_path = 'data/risk_models/cardiovascular_model.pkl'
joblib.dump(best_model_cardio, cardio_model_path)
print(f"\n✓ Best Cardiovascular Model Saved: {cardio_model_path}")
print(f" Accuracy: {best_score_cardio:.4f}")
except Exception as e:
print(f"✗ Error training cardiovascular model: {e}")
# ============================================================================
# MODEL 3: HYPERTENSION RISK PREDICTION
# ============================================================================
print("\n[3/3] TRAINING HYPERTENSION RISK MODEL...")
print("-" * 70)
try:
df_chronic = pd.read_csv('chronic_disease_prediction_dataset.csv')
print(f"✓ Loaded chronic disease dataset: {df_chronic.shape}")
# Drop Patient_ID
df_chronic = df_chronic.drop('Patient_ID', axis=1)
# Encode categorical variables
le_dict = {}
categorical_cols = df_chronic.select_dtypes(include=['object']).columns
for col in categorical_cols:
if col != 'HasChronicDisease':
le = LabelEncoder()
df_chronic[col] = le.fit_transform(df_chronic[col])
le_dict[col] = le
# Target: HasChronicDisease (as proxy for hypertension)
le_target = LabelEncoder()
y_hypertension = le_target.fit_transform(df_chronic['HasChronicDisease'])
X_hypertension = df_chronic.drop('HasChronicDisease', axis=1)
# Split data
X_train, X_test, y_train, y_test = train_test_split(
X_hypertension, y_hypertension, test_size=0.2, random_state=42, stratify=y_hypertension
)
# Scale features
scaler_hyper = StandardScaler()
X_train_scaled = scaler_hyper.fit_transform(X_train)
X_test_scaled = scaler_hyper.transform(X_test)
# Train multiple models
models_hyper = {
'Logistic Regression': LogisticRegression(max_iter=1000, random_state=42),
'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1),
'Gradient Boosting': GradientBoostingClassifier(n_estimators=100, random_state=42)
}
best_model_hyper = None
best_score_hyper = 0
results_hyper = {}
for name, model in models_hyper.items():
model.fit(X_train_scaled, y_train)
y_pred = model.predict(X_test_scaled)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, zero_division=0)
recall = recall_score(y_test, y_pred, zero_division=0)
f1 = f1_score(y_test, y_pred, zero_division=0)
results_hyper[name] = {
'accuracy': accuracy,
'precision': precision,
'recall': recall,
'f1': f1
}
print(f"\n {name}:")
print(f" Accuracy: {accuracy:.4f}")
print(f" Precision: {precision:.4f}")
print(f" Recall: {recall:.4f}")
print(f" F1-Score: {f1:.4f}")
if accuracy > best_score_hyper:
best_score_hyper = accuracy
best_model_hyper = model
# Save best hypertension model
hyper_model_path = 'data/risk_models/hypertension_model.pkl'
joblib.dump(best_model_hyper, hyper_model_path)
print(f"\n✓ Best Hypertension Model Saved: {hyper_model_path}")
print(f" Accuracy: {best_score_hyper:.4f}")
except Exception as e:
print(f"✗ Error training hypertension model: {e}")
# ============================================================================
# SUMMARY
# ============================================================================
print("\n" + "=" * 70)
print("MODEL TRAINING SUMMARY")
print("=" * 70)
print(f"\n✓ Diabetes Model: Accuracy = {best_score_diabetes:.4f}")
print(f"✓ Cardiovascular Model: Accuracy = {best_score_cardio:.4f}")
print(f"✓ Hypertension Model: Accuracy = {best_score_hyper:.4f}")
print(f"\nAll models saved to: data/risk_models/")
print("\n" + "=" * 70)
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