An Interpretable Machine Learning Framework: Improving Diagnostic Efficiency In Early Chronic Disease Detection

Sam Jackson S^1*, Christina Magneta S²

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ABSTRACT

Early identification of chronic diseases continues to be a major challenge in healthcare, particularly when conditions progress silently during their initial stages. Chronic Kidney Disease (CKD) exemplifies this issue, as declining renal function often remains clinically unnoticed until the disease reaches an advanced and less manageable phase. Delayed diagnosis significantly restricts treatment options and increases the likelihood of irreversible complications, highlighting the need for reliable and clinically meaningful decision-support systems. This study presents an interpretable machine learning–based diagnostic framework aimed at enhancing early detection of chronic diseases, with a focused application to CKD. The proposed framework follows a systematic and reproducible learning pipeline that combines comprehensive data preprocessing, ensemble learning, and model interpretability. A Random Forest classifier is adopted as the primary predictive model due to its ability to effectively manage heterogeneous clinical features and capture non-linear relationships within medical data. To mitigate the class imbalance commonly encountered in clinical datasets, the Synthetic Minority Oversampling Technique (SMOTE) is incorporated during the training process. Model performance is optimized using stratified cross-validation and evaluated through clinically relevant metrics to ensure diagnostic reliability beyond overall accuracy. To promote transparency and foster clinical confidence, SHapley Additive exPlanations (SHAP) are employed to generate both global and patient-specific interpretations of model predictions. These explanations provide insight into feature importance and decision rationale, enabling alignment between model outcomes and established clinical understanding. The results demonstrate that the proposed framework offers a balanced combination of predictive performance and interpretability, making it a promising tool for early CKD diagnosis and broader clinical decision support applications.

Keywords: Chronic Kidney Disease; early disease detection; interpretable machine learning; explainable artificial intelligence; Random Forest classification; clinical decision support systems; healthcare analytics

How to cite this article: Sam Jackson S, Christina Magneta S. An Interpretable Machine Learning Framework: Improving Diagnostic Efficiency In Early Chronic Disease Detection. Int J Drug Deliv Technol. 2026;16(16s): 279-284. DOI: 10.25258/ijddt.16.16s.30

Source of support: Nil.

Conflict of interest: None