Jata Shankar, Aniket Dubey, Vijay Kage, Sahil Dhane

DOI: https://doi.org/10.5281/zenodo.23540242

Early prediction of Chronic Kidney Disease (CKD) and liver disease can support timely medical assessment and improve disease management. However, identifying these conditions through clinical and laboratory data can be challenging due to variations in patient characteristics and the complexity of medical attributes. This study proposes an AI-Based Early Disease Prediction System using machine learning techniques to analyse CKD and liver disease datasets. Five algorithms—Logistic Regression, Decision Tree, Random Forest, XGBoost, and LightGBM—will be evaluated to compare their classification performance for both diseases. Data preprocessing techniques will be applied to handle missing values, encode categorical features, and prepare the datasets for model training and testing. Performance will be assessed using accuracy, precision, recall, F1-score, and ROC-AUC, where applicable. In addition, SHapley Additive exPlanations (SHAP) will be used to interpret model predictions and identify features contributing to the output. The proposed framework aims to provide a comparative and interpretable approach to preliminary disease-risk prediction. As a research prototype, it may support healthcare research and early risk assessment but is not intended to replace professional diagnosis. Further testing and independent clinical validation will be necessary before practical clinical use.

Artificial Intelligence, Chronic Kidney Disease, Liver Disease, Machine Learning, SHAP, Explainable AI.

The proposed AI-Based Early Disease Prediction System aims to support the preliminary prediction of Chronic Kidney Disease (CKD) and liver disease using machine learning techniques. By comparing Logistic Regression, Decision Tree, Random Forest, XGBoost, and LightGBM, the study will examine the performance of different algorithms on disease-specific datasets. The integration of SHAP will provide explanations of feature contributions, making model predictions easier to interpret. The system may contribute to healthcare research by combining predictive modelling with explainable AI. However, its reliability will depend on data quality, experimental results, and validation on independent datasets. Future work should focus on testing the models with larger and more diverse patient data, improving generalizability, and assessing their suitability for clinical settings. The proposed framework is intended to assist preliminary risk assessment and research, not replace medical professionals or clinical diagnosis.

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Jata Shankar, Aniket Dubey, Vijay Kage, Sahil Dhane (2026). AI-Based Early Disease Prediction System: A Comparative and Explainable Machine Learning Approach for Chronic Kidney and Liver Diseases. International Journal of Computer Techniques, 13(5), 562–566. ISSN: 2394-2231. DOI: https://doi.org/10.5281/zenodo.23540242