Prediction of Abnormality in Kidney Function Using Classification Techniques and Fuzzy Systems

Kidney diseases are life threatening. Its development is prevented by early detection and vigorous management. It is important to discover such disorders at an early stage in order to extend a patient's lifespan and to classify the abnormalities in kidney function based on pathological data. The primary goal is to identify the stages of the kidney disease and check the performance for various classifiers of the model. In this paper, classification algorithms are used to find out the accuracy of the supervised data. Not all machine learning classifiers predict the accurate results because of imprecision. So, fuzzy expert system (FES) is used to deal with imprecise data. To predict the disease at an early stage and also to identify the stages of the disease, FES is used. FES has shown promising results in identifying the stages of the patients. The accuracy of the pathological data is found by using machine learning algorithms. In addition, the probability of the occurrence of the disease is found by combining various parameters and identified the stages of the patient’s disease.