Support Vector Machine based eHealth Cloud System for Diabetes Classification

Abstract

INTRODUCTION: Diabetes is a major health issue because it leaves people with physical disabilities. Therefore, methodologies with a reduced error rate must be used to diagnose this dangerous disease. Data Mining techniques such as Artificial Neural Network are common tools adopted for the classification of diabetes and one of the core components of the eHealth system. Data Mining techniques aim to provide reliable and timely diagnostic outcomes during the diagnosis of the disease.

OBJECTIVE: The objective of the research work is to propose a Support Vector Machine based eHealth Cloud System for Diabetes Classification. This work aims to improve the diagnostic accuracy of computer-assisted diagnostic systems.

METHOD: The proposed methodology implemented in two-phase, In the first phase system is trained using different Support Vector Machine (SVM) kernel functions and in the second phase effectiveness of the system is tested in terms of classification accuracy and error. Different SVMs have the ability to diagnose this disease. PIMA Indian Diabetes Dataset (PIDD) has been used in our experiments for training and testing. Kernel functions are usually used to refer to the kernel trick, a method of using a linear classifier to solve a non-linear problem.

RESULT: In this classification accuracy and classification error are used for performance evaluation. It is worth mentioning that the system giving remarkable accuracy of 77.50% in Coarse Gaussian SVM in 10-fold validation whereas fine Gaussian SVM gives 98.8% accuracy in No validation set.

CONCLUSION: This paper introduces the SVM eHealth Cloud System for Diabetes Classification. The system is trained using the PIDD. Such a system can be used as “Application-as-a-Service” in cloud computing. It is therefore believed that the system will enhance the process of clinical decision-making and also assist physicians concerning Diabetes Diagnosis. It is worth to mention that the SVM kernel-based system performed well in comparison to the different systems.