A Supervised Learning Approach To Diagnose Cytitis And Nephritis

The role of supervised machine learning approaches in the domain of medical diagnosis has been a prominent issue among researchers from last several years. In this article, we have modeled a supervised learning technique using Neural Networks to diagnose Cystitis and Nephritis. The feature set comprises of laboratory test outcomes conducted at Sharda Hospital to investigate these diseases. The performance of learning is enhanced using naive bayes classifier and hyperparameter estimation using cross-validation techniques. The experimental outcomes are promising in nature to justify or generalize decision space between Cystitis and Nephritis.