Machine Learning and Chronic Kidney Disease: Towards Early Prediction and Diagnosis

Abstract

In India, Chronic Kidney Disease (CKD) presents a formidable public health challenge, with regional prevalence rates showcasing a broad range from less than 1% to as high as 13%. This significant variability not only highlights the disease's widespread impact but also points to the diverse demographic and environmental factors at play. This paper delves into the potential of Machine Learning (ML) to revolutionize early CKD prediction, critically analyzing recent technological advancements, the utilization of various datasets, and the inherent challenges and limitations faced by existing predictive models. By offering a comprehensive overview of the current state of ML applications in CKD detection, the review identifies key areas where further research and development could significantly improve predictive accuracy and efficiency. Additionally, it provides targeted recommendations for future research, emphasizing the need for more robust, scalable, and interpretable ML models that can adapt to India's unique healthcare landscape.

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