An Unsupervised Learning Approach to Analyze the Feature Space of Cystitis and Nephritis

The analysis of feature space plays a vital role in making valid decisions in the bio-medical domain. In this article, we have investigated the feature space of laboratory tests conducted to diagnose “Cystitis” and “Nephritis”. The experimentation samples were acquired from laboratory reports generated at Sharda Hospital. Moreover, the feature space analysis is conducted using K-Means clustering and Neural Networks in an unsupervised mode. The experimental outcomes are promising in nature to understand the disease’s feature space that enhances the decision making.