An intelligent IoT with cloud centric medical decision support system for chronic kidney disease prediction. Issue 3 (20th March 2020)

• Record Type: Journal Article
• Authors: Arulanthu, Pramila; Perumal, Eswaran
• Is Part Of: International journal of imaging systems and technology. Volume 30:Issue 3(2020)
• Page Start: 815
• Page End: 827
• Publication Date: 2020-03-20
• Subjects: Adam -- cloud -- healthcare -- IoT -- logistic regression; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22424
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: 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.