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

Behnaz Naghash Almasi; Omid Naghash Almasi; Mina Kavousi; Amirhossein Sharifinia

Article Details

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

Authors:

Behnaz Naghash Almasi | Omid Naghash Almasi | Mina Kavousi | Amirhossein Sharifinia

Publisher:

Science Publishing Corporation

Journal:

Journal of Advanced Computer Science & Technology Vol.2 Issue 2, pp.68-76

Document Type:

Article

Publication Date:

11/2013

Document Language:

English

Document ID (Org No.):

2001388419

pISSN:

2227-4332

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.

Article Details

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

Authors: Behnaz Naghash Almasi | Omid Naghash Almasi | Mina Kavousi | Amirhossein Sharifinia

Publisher: Science Publishing Corporation

Journal: Journal of Advanced Computer Science & Technology Vol.2 Issue 2, pp.68-76

Document Type: Article

Publication Date: 11/2013

Document Language: English

Document ID (Org No.): 2001388419

pISSN: 2227-4332

Issues

Authors:

Behnaz Naghash Almasi | Omid Naghash Almasi | Mina Kavousi | Amirhossein Sharifinia

Publisher:

Science Publishing Corporation

Journal:

Journal of Advanced Computer Science & Technology Vol.2 Issue 2, pp.68-76

Document Type:

Article

Publication Date:

11/2013

Document Language:

English

Document ID (Org No.):

2001388419

pISSN:

2227-4332