A Machine Learning Based Early Warning Framework for CKDu Risk Prediction Using Water Quality Data

Abstract

This paper proposes and tests an environmental water-quality screening proof-of-concept based on machine learning and routine physicochemical measurements. Although the framework is described as CKDu risk prediction, it should be interpreted strictly as an environmental proxy water-potability feature-screening tool and not as a clinical patient-modelling system. The study employed an open-source dataset of 3,276 water samples with nine variables: pH, hardness, total dissolved solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes and turbidity. Missing values were imputed using median values, and feature scaling was applied where required. A stratified 80:20 train-test split was performed using the original class distribution and a random seed of 42. Accuracy, balanced accuracy, macro F1-score, ROC-AUC and confusion-matrix analysis were used to evaluate five supervised machine-learning models. Random Forest achieved the highest test performance, with an accuracy of 0.659, balanced accuracy of 0.641, macro F1-score of 0.636 and ROC-AUC of 0.695. The selected model was embedded in a prototype interface that translates non-potability probabilities into Low, Moderate and High screening bands and provides input validation and user-friendly follow-up messages. The results indicate the technical feasibility of multivariate machine learning for water-potability classification and sample prioritisation. The dataset does not include CKDu patient records, clinical outcomes, patient exposure histories, geographic exposure information or locally collected samples from CKDu-endemic communities. Therefore, the framework should be regarded only as a proof-of-concept environmental water-screening tool, not as a validated CKDu prediction, clinical diagnostic or regulatory decision-making system.