At present days, Internet of Things (IoT) and cloud platforms become widely used in various healthcare applications. The enormous quantity of data produced by the IoT devices in the healthcare sector can be examined on the cloud platform instead of dependent on restricted storage and computation resources exist in the mobile gadgets. For offering effective medicinal services, in this article, an online medical decision support system (OMDSS) is introduced for chronic kidney disease (CKD) prediction. The presented model involves a set of stages namely data gathering, preprocessing, and classification of medical data for the prediction of CKD. For classification, logistic regression (LR) model is applied for classifying the data instances into CKD and non‐CKD. In addition, for tuning the parameters of LR, Adaptive Moment Estimation (Adam), and adaptive learning rate optimization algorithm is applied. The performance of the introduced model is examined using a benchmark CKD dataset. The experimental outcome observed the superior characteristics of the presented model on the applied dataset.

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.

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