Smart Detection Machine Learning for Affordable Chronic Kidney Disease Screening

Abstract

Chronic kidney disease (CKD) is a major global health issue, one that is increasingly prevalent in both advanced and emerging economies. Especially in under-resourced settings, the lack of specialized tools greatly complicates early diagnosis. This study proposes some methods for machine learning (ML) to determine key features of CKD that could be integrated into low-cost diagnostic screening tools accessible to primary level healthcare providers We use and test various machine learning approaches such as Random Forest and Support Vector Machine (SVM) and a custom ensemble model to analyze a large set of clinical factors for the disease. In this case, the provided framework achieves CKD prediction with 97.8% accuracy, 96.5% sensitivity, and 98.4% specificity from just the most basic clinical parameters available — outcomes that are achievable by any standard. Our research illustrates that diagnostic tools based on ML can accurately assess vulnerabilities for CKD with Fundamental clinical indicators can improve patient care and speed up response time in resource-constrained environments. Effective implementation provides a proof- of-concept that balances diagnostic precision with reasonable access, showing it is adaptable within existing healthcare systems.