Advanced Machine Learning Approaches for Enhanced Chronic Kidney Disease Detection and Stage Classification in Clinical Settings

chronic kidney disease constitutes a gradual decline in renal function over extended timeframes, presenting substantial diagnostic challenges in contemporary medical practice. This investigation introduces a novel computational framework utilizing custom-developed machine learning algorithms for enhanced CKD identification and automated severity assessment. The research emphasizes ground-up algorithm development using C#, .NET, and ASP.NET technologies, avoiding dependency on external machine learning libraries. Our methodology incorporates proprietary feature selection mechanisms integrated with bespoke classification algorithms to establish a comprehensive diagnostic platform. The system not only determines disease presence but also delivers individualized dietary recommendations derived from patient-specific laboratory values, with particular emphasis on electrolyte management. This implementation showcases substantial promise for transforming clinical decision-making workflows within nephrology departments through direct hospital server deployment.