Machine Learning Model for Binary Classification Based Prediction of Chronic Renal Disease

Abstract

In emerging and underdeveloped nations, chronic kidney disease can be detected early thanks to machine learning-based medical diagnostics. Machine learning models are utilized to identify renal disease using a real-time dataset. The proposed model was trained and tested in the study's research using the renal disease dataset. The proposed model's experimental findings seem to support its promised level of prediction accuracy. Machine learning can recognize patterns linked to particular diseases using patient electronic medical records and can notify clinicians of any irregularities. To predict renal disease, a variety of feature selection and classification techniques are used. Predicting kidney disease will help you prevent developing early renal failure.