A Diabetes Monitoring System and Health-Medical Service Composition Model in Cloud Environment

Abstract

Diabetes is a common chronic illness or absence of sugar in the blood. The early detection of this disease decreases the serious risk factor. Nowadays, Machine Learning based cloud environment acts as a vital role in disease detection. The people who belong to the rural areas are not getting the proper health care treatments. So, this research work proposed an automated eHealth cloud system for detecting diabetes in the earlier stage to decrease the mortality rate and provides health treatment facilities to rural peoples. Extreme Learning Machine (ELM) is a type of Artificial Neural Network (ANN) that has a lot of potential for solving classification challenges. This research work is consisting of several activities like feature normalization, feature selection and classification. We have employed principal component analysis (PCA) for feature selection and extreme learning machine (ELM) for classification. Finally, a cloud computing-based environment with three numbers of virtual machines (vCPU-4, vCPU-8, and vCPU-16), is used for the detection of diabetes. The efficacy of the proposed model has been evaluated with the PIMA dataset in both standalone and cloud environments and achieved 90.57 % accuracy, 82.24 % sensitivity, 73.23 % specificity, and 75.03 % F-1 score with the virtual machine vCPU-16. The experimental results define the proposed model as superior to other state-of-art models with better classification accuracy and less number of features.

Keywords

• Random forest
• K-nearest neighbour
• extreme learning machine
• principal component analysis
• attribute weighted artificial immune system
• diabetes mellitus