PERSONALISED HEALTHCARE RECOMMENDATION SYSTEM USING XGBOOST AND SHAP

Abstract: The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has brought significant improvements to healthcare by supporting disease prediction, individual health assessment, and clinical decision- making. However, conventional healthcare systems often provide generalized recommendations without adequately considering individual differences in demographic information, lifestyle, physiological conditions, medical history, and health risks. This review proposes an AI-Based Personalized Healthcare Recommendation System that integrates Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP). The approach combines the strong predictive performance of XGBoost with Explainable Artificial Intelligence (XAI) to generate personalized and understandable healthcare insights. The system analyzes various healthcare attributes, including demographic details, body measurements, vital signs, lifestyle factors, medical history, family history, laboratory results, and existing health conditions to assess potential disease risks. XGBoost is effective for structured healthcare data as it can capture complex nonlinear relationships between different health-related features. SHAP provides explanations by identifying the contribution of individual features to each prediction, enabling users and healthcare professionals to understand important factors such as blood glucose, body mass index, cholesterol levels, physical activity, sleep duration, and family history.

Keywords: Personalized Healthcare, Machine Learning, XGBoost, SHAP