A diagnostic prediction model for chronic kidney disease in internet of things platform

Chronic Kidney Disease (CKD) is being typically observed as a health threatening issue, especially in developing countries, where receiving proper treatments are very expensive. Therefore, early prediction of CKD that protects the kidney and breaks the gradual progress of CKD has become an important issue for physicians and scientists. Internet of Things (IoT) as a useful paradigm in which, low cost body sensor and smart multimedia medical devices are applied to provide remote monitoring of kidney function, plays an important role, especially where the medical care centers are hardly available for most of people. To gain this objective, in this paper, a diagnostic prediction model for CKD and its severity is proposed that applies IoT multimedia data. Since the influencing features on CKD are enormous and also the volume of the IoT multimedia data is usually very huge, selecting different features based on physicians� clinical observations and experiences and also previous studies for CKD in different groups of multimedia datasets is carried out to assess the performance measures of CKD prediction and its level determination via different classification techniques. The experimental results reveal that the applied dataset with the proposed selected features produces 97 accuracy, 99 sensitivity and 95 specificity via applying decision tree (J48) classifier in comparison to Support Vector Machine (SVM), Multi-Layer Perception (MLP) and Naïve Bayes classifiers. Also, the proposed feature set can improve the execution time in comparison to other datasets with different features. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.