Explainable xgboost framework for multi-class disease severity prediction: a clinical machine learning study with shap-based interpretability

Non-communicable diseases (NCDs) are a major growing problem, and responsible for around 74% of all deaths globally. Early and correct stratification of the severity of the disease is essential for timely therapeutic interventions, efficient use of clinical resources and patient benefits. Traditional Clinical Scoring Systems (CSSs) like APACHE II and SOFA are based on handcrafted and limited features and miss the complex and non-linear interactions between variables in multi-morbid patients. In this study, a novel and interpretable machine learning (ML) pipeline is developed based on extreme gradient boosting (XGBoost) model for four class disease severity prediction (Mild, Moderate, Severe, Critical). A retrospective multi-centre clinical dataset of 12450 patient records was collected between 2018-2024 from three tertiary care hospitals of Maharashtra, india and de-identified for analysis. Data preprocessing involved multiple imputation by chained equations (MICE) for missing value imputation, target encoding for categorical features and robust scaler normalization and synthetic minority oversampling technique (SMOTE) for handling class imbalance. The hyper parameters of XGBoost w