import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score, classification_report

# Load dataset (replace with actual dataset path)
df = pd.read_csv("healthcare_data.csv")

# Assume target column is 'Risk' (0: Low, 1: High) and drop non-numeric or identifier columns
y = df['Risk']
X = df.drop(columns=['Risk', 'PatientID'])

# Handle missing values (simple imputation with mean)
X = X.fillna(X.mean())

# Split dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Standardize features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Model 1: Logistic Regression
log_model = LogisticRegression()
log_model.fit(X_train_scaled, y_train)
y_pred_log = log_model.predict(X_test_scaled)
print("Logistic Regression Performance:")
print(classification_report(y_test, y_pred_log))

# Model 2: Random Forest
rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)
y_pred_rf = rf_model.predict(X_test)
print("Random Forest Performance:")
print(classification_report(y_test, y_pred_rf))

# Model 3: XGBoost
xgb_model = XGBClassifier(use_label_encoder=False, eval_metric='logloss')
xgb_model.fit(X_train, y_train)
y_pred_xgb = xgb_model.predict(X_test)
print("XGBoost Performance:")
print(classification_report(y_test, y_pred_xgb))

# Compare Model Accuracies
print("Model Accuracies:")
print(f"Logistic Regression: {accuracy_score(y_test, y_pred_log):.4f}")
print(f"Random Forest: {accuracy_score(y_test, y_pred_rf):.4f}")
print(f"XGBoost: {accuracy_score(y_test, y_pred_xgb):.4f}")