An expert system for optimising thyroid disease diagnosis
by Palanichamy Jaganathan; Nallamuthu Rajkumar
International Journal of Computational Science and Engineering (IJCSE), Vol. 7, No. 3, 2012

Abstract: Thyroid diseases have become common disorders worldwide. Thyroid is a gland in the neck that controls and stimulates the metabolism of the body. Hyperthyroidism (over production of thyroid hormone) and hypothyroidism (less production of thyroid hormone) are the most common problems of thyroid. In this study, improved F-score feature selection method is used to select the most relevant features for classification of thyroid dataset. The average improved F-score value of the features computed is set as the cut-off for selecting the features. The features above the cut-off are chosen as more relevant than others. Then the selected features were used in thyroid disease diagnosis with multilayer perceptron (MLP) and C4.5 algorithm and compared with recent research results obtained from generalised discriminant analysis-wavelet support vector machine (GDA-WSVM) technique (91.86%). The results show that our new feature selection method applied to this dataset has produced better classification accuracy than GDA-WSVM combination, with an improvement of 1.63% of accuracy for improved F-score-MLP combination (93.49%).

Online publication date: Mon, 22-Sep-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com