Chronic Kidney Disease Detection Using Hybrid Machine Learning

This research presents a ML–based methodology for diagnosing CKD using clinical attributes. Through systematic feature selection, preprocessing, and algorithm comparison, the hybrid machine learning classifier demonstrated superior diagnostic performance. The proposed system can be integrated into clinical decision support tools for early CKD detection. Machine learning has significant potential to enhance CKD diagnosis by enabling early and accurate detection, thus facilitating timely treatment. The study's proposed model not only supports clinical decision-making but also paves the way for further integration of AI in nephrology Future work includes developing a real-time CKD prediction mobile application and integrating deep learning models with electronic health records.

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