Computer–aided diagnosis of diabetes using least square support vector machine

Authors

  • Behnaz Naghash Almasi

    Department of Medical Science, Faculty of Nursing and Midwifery, Islamic Azad University, Mashhad Branch, Iran
  • Omid Naghash Almasi

    Department of Electrical Engineering, Islamic Azad University, Gonabad Branch, Iran
  • Mina Kavousi

    Department of Electrical Engineering, Semnan University, Semnan, Iran
  • Amirhossein Sharifinia

    Department of Electrical Engineering, Islamic Azad University, Mashhad Branch, Iran

Received date: August 2, 2013

Accepted date: August 31, 2013

Published date: November 13, 2013

DOI:

https://doi.org/10.14419/jacst.v2i2.1194

Abstract

Diabetes incidence is one of the most serious health challenges in both industrial and developing countries; however, it is for sure that the early detection and accurate diagnosis of this disease can decrease the risk of affiliation to other relevant disease in diabetes patients. Because of the effective classification and high diagnostic capability, expert systems and machine learning techniques are now gaining popularity in this field. In this study, Least square support vector machine (LS-SVM) was used for diabetes diagnosis. The effectiveness of the LS-SVM is examined on Pima Indian diabetes dataset using k-fold cross validation method. Compared to thirteen well-known methods for the diabetes diagnosis in the literature, the study results showed the effectiveness of the proposed method.

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How to Cite

Almasi, B. N., Almasi, O. N., Kavousi, M., & Sharifinia, A. (2013). Computer–aided diagnosis of diabetes using least square support vector machine. Journal of Advanced Computer Science & Technology, 2(2), 68-76. https://doi.org/10.14419/jacst.v2i2.1194

Received date: August 2, 2013

Accepted date: August 31, 2013

Published date: November 13, 2013