Indian Journal of Science and Technology

Abstract

Objectives: Presently, smart healthcare applications utilizing Internet of Things (IoT) offers vast number of features and real time services. They offer a real platform for billions of users to receive regular information related to health and better lifestyle. The usage of IoT components in the medicinal domain greatly helps to implement diverse characteristics of these applications. Methods: The huge volume of data created by the IoT devices in medicinal field is investigated on the cloud rather than mainly depends on available memory and processing resources of handheld devices. Keeping this idea in mind in this study, we try to devise an IoT and cloud based smart healthcare system to diagnose the disease. The IoT devices attached to the patient body gathers the needed data and stored in the cloud. Then, we present an optimal Support Vector Machine with Grey Wolf Optimization (SVM-GWO) algorithm to classify the presence of disease using the acquired data. For experimentation, we employ a benchmark heart disease dataset and a set of measures are used to analyze the attained results. Findings: The presented SVM-GWO achieves a maximum classifier results with accuracy of 84.07%, precision, recall and F-score of 84.10% respectively. Novelty: An optimal Support Vector Machine with Grey Wolf Optimization (SVMGWO) algorithm is used to classify the presence of disease using the acquired data. The experimental outcome ensures the betterment of the presented model over the compared methods under different evaluation parameters.

Keywords: Cloud, Healthcare, IoT, Support Vector Machine