Methods Of Recognizing Chronic Kidney Disease Using Machine Learning

Abstract

Machine learning-based medical diagnostics will allow for the early identification of CKD in developing and disadvantaged countries. Using a real-time dataset, machine learning models are used to diagnose renal illness. Kidney failure develops as a result, and while detecting CKD in its early stages might be challenging, it is critical. Early CKD diagnosis must be prioritized by screening of persons with diabetes, hypertension, autoimmune illnesses, or a family history of the condition. The kidney disease dataset was utilized to train and test the proposed model in the study's research. At the conclusion, we evaluated the predictive power of the proposed model. The suggested model's experimental findings seem to support its promised level of prediction accuracy.