Fuzzy unordered rule using greedy hill climbing feature selection method: an application to diabetes classification. Journal of Information and Communication Technology, 20 (3). pp. 391-422. ISSN 2180-3862 (2021)
Abstract
Diabetes classification is one of the most crucial applications of healthcare diagnosis. Even though various studies have been conducted in this application, the classification problem remains challenging. Fuzzy logic techniques have recently obtained impressive achievements in different application domains especially medical diagnosis. Fuzzy logic technique is not able to deal with data of a large number of input variables in constructing a classification model. In this research, a fuzzy logic technique using greedy hill climbing feature selection methods was proposed for the classification of diabetes. A dataset of 520 patients from the Hospital of Sylhet in Bangladesh was used to train and evaluate the proposed classifier.
| Item Type: | Article |
|---|---|
| Keywords: | Data mining, Machine Learning, Fuzzy logic, Application software, Classification model |
| Taxonomy: | By Subject > Computer & Mathematical Sciences > Computer Science By Subject > Computer & Mathematical Sciences > Information Technology |
| Local Content Hub: | Subjects > Computer and Mathematical Sciences |
| Depositing User: | Muslim Ismail @ Ahmad |
| Date Deposited: | 21 Feb 2022 23:32 |
| Last Modified: | 22 Feb 2022 08:47 |
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