A Machine Learning Model for Predicting of Chronic Kidney Disease Based Internet of Things and Cloud Computing in Smart Cities

Abstract

Cloud computing and internet of things (IOT) plays an important role in health care services especially in the prediction of diseases in smart cities. IOT devices (digital sensors and etc.) can be used to send big data onto chronic kidney diseases (CKD) to store it in the cloud computing. Therefore, these big data are used to increase the accuracy of prediction of CKD on cloud environment. The prediction of dangerous diseases such as CKD based cloud-IOT is considered a big problem that facing the stakeholders of health cares in smart cities. This paper focuses on predicting of CKD as an example of health care services on cloud computing environment. Cloud computing is supported patients to predict of CKD anywhere and anytime in smart cities. For that, this paper proposes a hybrid intelligent model for predicting CKD based cloud-IOT by using two intelligent techniques, which are linear regression (LR) and neural network (NN). LR is used to determine critical factors that influence on CKD. NN is used to predict of CKD. The results show that, the accuracy of hybrid intelligent model in predicting of CKD is 97.8%. In addition, a hybrid intelligent model is applied on windows azure as an example of a cloud computing environment to predict of CKD to support patients in smart cities. The proposed model is superior to most of the models referred to in the related works by 64%.