Identification and prediction of kidney diseases by using machine learning techniques

Machine learning (ML) applications in health informatics are receiving more and more attention. One scenario that highlights the important function of ML diagnostic algorithms is the timely diagnosis of renal disease and the following prompt reaction to it. The goal of ML in Kidney Disease Diagnosis (MLKDD), an active research area, is to help doctors diagnose kidney diseases using computer-aided systems. Numerous studies have attempted to evaluate the viability, applicability, and superiority of various ML techniques over one another. This study uses Convolutional Neural Networks (CNN) and other machine learning techniques to determine healthy or patient individuals from medical photographs. We used CNN to evaluate a number of machine learning techniques, including Naive Bayes, K- Nearest Neighbour, Decision Trees, Support Vector Machines, and deep neural networks. The experiments show that the CNN Classifier produces the best classification outcomes.