Intelligent Chronic Kidney Disease Diagnosis System using Cloud Centric Optimal Feature Subset Selection with Novel Data Classification Model

Abstract

Abstract Internet of Things (IoT) and cloud computing offers diverse applications in the medicinal sector by the integration of sensing and therapeutic gadgets. Medical expenses are rising gradually and different new diseases also exist globally, it becomes essential to transform the healthcare facilities from a hospital to patient-centric platform. For providing effective remote healthcare services to patients, this paper introduces an optimal IoT and cloud based decision support system for Chronic Kidney Disease (CKD) diagnosis. The proposed method makes use of simulated annealing (SA) based feature selection (FS) with Root Mean Square Propagation (RMSProp) Optimizer based Logistic Regression (LR) model called SA-RMSPO-LR to classify the existence of CKD from medical data. The proposed model involves a set of four subprocesses, which include data collection, preprocessing, FS, and classification. The inclusion of SA for FS helps to improvise the classifier results of the SA-RMSPO-LR model. The effectiveness of the SA-RMSPO-LR model has been validated using a benchmark CKD dataset. The experimental results indicated that the proposed SA-RMSPO-LR model leads to effective CKD classification with the maximum sensitivity of 98.41%, specificity of 97.99%, accuracy of 98.25%, F-score of 98.60% and kappa value of 96.26%. The experimental outcome indicates that the proposed SA-RMSPO-LR model has the capability to detect and classify CKD over the compared methods proficiently.