MABONGALABS/HEMA_CLASS

Hemaclass AI Diagnostics: Clinical Decision Support System

An explainable ensemble-based diagnostic tool optimized for Malaria, Sickle Cell Anemia (SCA), and Co-infection classification in Western Kenya.

🔬 Model Details

• Architecture: Stacking Ensemble (RF, SVM, XGBoost) with a Logistic Regression Meta-Learner.
• Explainability: Integrated SHAP waterfall plots for local feature importance.
• Preprocessing: MICE Imputation, SMOTE balancing, and Z-Score Normalization.

🚀 Quick Usage (Inference Code)

To use this model programmatically, ensure you have your .pkl artifacts in the local directory.

import joblib
import pandas as pd
import numpy as np

# 1. Load Artifacts
model = joblib.load('ensemble_model.pkl')
scaler = joblib.load('scaler.pkl')
imputer = joblib.load('imputer.pkl')
FEATURES = joblib.load('feature_names.pkl')
target_names = ['Negative', 'Malaria', 'SCA', 'Co-infection']

def predict_patient(data_dict):
    """
    Input: Dictionary of patient vitals/labs
    Output: Predicted Diagnosis and Confidence
    """
    # Create DataFrame and align features
    df = pd.DataFrame([data_dict])
    for col in set(FEATURES) - set(df.columns):
        df[col] = np.nan
    df = df[FEATURES]

    # Preprocess
    X_imp = imputer.transform(df)
    X_scaled = scaler.transform(X_imp)

    # Inference
    pred = model.predict(X_scaled)
    prob = np.max(model.predict_proba(X_scaled))
    
    return {
        "Diagnosis": target_names[pred],
        "Confidence": f"{prob*100:.2f}%"
    }

# Example Usage:
patient_data = {'age': 25, 'hb': 10.5, 'temp': 38.5, 'malaria_rdt': 1}
print(predict_patient(patient_data))