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

Abstract

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.

Introduction

The study addresses Chronic Kidney Disease (CKD) as a major global health issue, emphasizing its asymptomatic progression and systemic complications. Traditional diagnostic methods are limited by inconsistencies and delayed detection. The growing prevalence of CKD, particularly in diabetic and hypertensive populations, highlights the need for automated, real-time prediction systems.

Literature Review

Previous research applied various machine learning (ML) and deep learning techniques for CKD prediction, but most were limited by:

• Use of small, static datasets (mainly from UCI repository)

• Prototype-level implementations

• Lack of real-time deployment

• Heavy reliance on third-party ML libraries

These gaps point to the need for custom, real-time, hospital-integrated solutions.

Proposed Methodology

The proposed system introduces a fully custom-developed CKD prediction and staging framework using:

• C#, .NET, and ASP.NET (no external ML libraries)

• Custom-built algorithms (Bayesian, KNN, SVM, Decision Trees)

• Real-time classification and dietary recommendation generation

• Integration with SQL Server for dynamic data handling

Implementation & Features

• Algorithms coded from scratch

• High accuracy (91.78%) with low false-negative rate (8.22%)

• Fast processing time (~1.6 seconds per prediction)

• Tailored nutritional recommendations

• Hospital server deployment enables real-time clinical use

Clinical Impact

• Supports physicians with data-driven, early CKD detection

• Cost-effective and adaptable to resource-limited settings

• Promotes preventive healthcare by enabling early interventions

Limitations & Future Work

• Requires larger, more diverse datasets

• Plans include:

  • Expanded biomarker integration

  • Mobile and IoT support

  • Cloud scalability

  • Broader language and hospital system compatibility

Conclusion

The development of this intelligent CKD detection system represents a substantial breakthrough in computer-assisted medical diagnostics through complete custom algorithm development. Through implementation of entirely proprietary Bayesian classification models developed from mathematical foundations using C#, .NET, and ASP.NET technologies, the framework successfully demonstrates its capacity to analyse essential biomarkers and laboratory parameters for comprehensive renal function evaluation. The system architecture enables seamless hospital server integration while preserving computational efficiency and diagnostic dependability through custom-programmed solutions eliminating external library dependencies. The complete custom implementation approach ensures institutional control over diagnostic algorithms while maintaining superior performance standards through ground-up development methodologies. Medical practitioners can utilize this technology to augment their clinical decision-making capabilities, providing medical professionals with a dependable supplementary instrument for early detection and continuous evaluation of kidney disease advancement. The comprehensive performance characteristics, with 91.78% accuracy and rapid processing times, confirm the system\'s readiness for clinical implementation across diverse healthcare settings.

References

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Copyright

Copyright © 2025 Harshavardhan H S, Sindu M S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.