Predicting Diabetes Mellitus Using Modified Support Vector Machine with Cloud Security

Abstract

Diabetes mellitus is one of the major concerned diseases that cause a large number of deaths every year. It is considered as the chronic disease which is caused by an increase in blood sugar. If diabetes remains unidentified and untreated, it creates more complexities. So, the early prediction of diabetes can reduce the fatal rate of a human. The data mining concept assists to diagnose diabetes. Various research studies are presented with various data mining algorithms for early prediction and disease diagnosis but still with lack of accuracy. At the same time, mining the diabetes data in a secure manner is one of the critical issues. To recover this issue, this paper designs the new model for early prediction of diabetes with high accuracy. This research explores the enhanced principal component analysis for efficient feature extraction from the dataset. To achieve the highest accuracy of classification, it has proposed the machine learning algorithm, namely, modified support vector machine (MSVM) which is used to detect the diabetes disease at an early stage. The main contribution of this research is to mining the patient’s disease results in cloud security. For this security purpose, honey bee encryption and decryption algorithm is used. The performance measures of the proposed method are evaluated on various measures of accuracy, sensitivity, specificity, precision, and negative predictive value. Results obtained show the proposed MSVM classifier outperforms with the highest accuracy of 97.13%. We have compared the proposed methods with existing methods for proving our method has better performance.

KEYWORDS:

• Principal Component analysis
• Support vector machine
• Diabetes mellitus
• Honey bee encryption
• Decryption

Additional information

Notes on contributors

S. Thenappan

S Thenappan is working as an assistant professor in Suguna College of Engineering, Coimbatore, India. He has completed his UG in ECE and PG in VLSI design from Karpagam University, Coimbatore, India. His current research interests include VLSI, soft computing techniques, cloud computing, and big data analysis. He is a life member of ISTE.

M. Valan Rajkumar

M Valan Rajkumar is working as a professor at Gnanamani College of Technology, Namakkal, India. He has completed his UG in EEE and PG in power electronics and drives from Anna University, Tiruchirappalli and received PhD degree from Anna University, Chennai, India. He has published more than 50 papers in international journals and conferences. His research interests include solar energy, power electronics, VLSI, soft computing techniques, cloud computing, and big data analysis. Email: [email protected]

P. S. Manoharan

P S Manoharan is working as an associate professor in Thiagarajar College of Engineering, Madurai, India. He has completed his UG in EEE and PG in power system engineering from Thiagarajar College of Engineering, Madurai and received PhD degree from Anna University, Chennai, India. He has published more than 125 papers in international journals and conferences. His research interests include solar energy, power system management, evolutionary computation, cloud computing, and big data analysis. Email: [email protected]