Fuzzy Unordered Rule Using Greedy Hill Climbing Feature Selection Method: An Application to Diabetes Classification
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...
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Online Access: | https://repo.uum.edu.my/id/eprint/28780/1/JICT%2020%2003%202021%20391-422.pdf https://doi.org/10.32890/jict2021.20.3.5 https://repo.uum.edu.my/id/eprint/28780/ https://e-journal.uum.edu.my/index.php/jict/article/view/14385 https://doi.org/10.32890/jict2021.20.3.5 |
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my.uum.repo.287802023-05-17T15:05:09Z https://repo.uum.edu.my/id/eprint/28780/ Fuzzy Unordered Rule Using Greedy Hill Climbing Feature Selection Method: An Application to Diabetes Classification Al-Behadili, Hayder Naser Khraibet Ku-Mahamud, Ku Ruhana QA75 Electronic computers. Computer science 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. Six classification criteria were considered to authenticate the results of the proposed classifier. Comparative analysis proved the effectiveness of the proposed classifier against Naive Bayes, support vector machine, K-nearest neighbour, decision tree, and multilayer perceptron neural network classifiers. Results of the proposed classifier demonstrated the potential of fuzzy logic in analyzing diabetes patterns in all classification criteria. Universiti Utara Malaysia Press 2021 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/28780/1/JICT%2020%2003%202021%20391-422.pdf Al-Behadili, Hayder Naser Khraibet and Ku-Mahamud, Ku Ruhana (2021) Fuzzy Unordered Rule Using Greedy Hill Climbing Feature Selection Method: An Application to Diabetes Classification. Journal of Information and Communication Technology, 20 (03). pp. 391-422. ISSN 2180-3862 https://e-journal.uum.edu.my/index.php/jict/article/view/14385 https://doi.org/10.32890/jict2021.20.3.5 https://doi.org/10.32890/jict2021.20.3.5 |
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QA75 Electronic computers. Computer science Al-Behadili, Hayder Naser Khraibet Ku-Mahamud, Ku Ruhana Fuzzy Unordered Rule Using Greedy Hill Climbing Feature Selection Method: An Application to Diabetes Classification |
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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. Six classification criteria were considered to authenticate the results of the proposed classifier. Comparative analysis proved the effectiveness of the proposed classifier against Naive Bayes, support vector machine, K-nearest neighbour, decision tree, and multilayer perceptron neural network classifiers. Results of the proposed classifier demonstrated the potential of fuzzy logic in analyzing diabetes patterns in all classification criteria. |
format |
Article |
author |
Al-Behadili, Hayder Naser Khraibet Ku-Mahamud, Ku Ruhana |
author_facet |
Al-Behadili, Hayder Naser Khraibet Ku-Mahamud, Ku Ruhana |
author_sort |
Al-Behadili, Hayder Naser Khraibet |
title |
Fuzzy Unordered Rule Using Greedy Hill Climbing Feature Selection Method: An Application to Diabetes Classification |
title_short |
Fuzzy Unordered Rule Using Greedy Hill Climbing Feature Selection Method: An Application to Diabetes Classification |
title_full |
Fuzzy Unordered Rule Using Greedy Hill Climbing Feature Selection Method: An Application to Diabetes Classification |
title_fullStr |
Fuzzy Unordered Rule Using Greedy Hill Climbing Feature Selection Method: An Application to Diabetes Classification |
title_full_unstemmed |
Fuzzy Unordered Rule Using Greedy Hill Climbing Feature Selection Method: An Application to Diabetes Classification |
title_sort |
fuzzy unordered rule using greedy hill climbing feature selection method: an application to diabetes classification |
publisher |
Universiti Utara Malaysia Press |
publishDate |
2021 |
url |
https://repo.uum.edu.my/id/eprint/28780/1/JICT%2020%2003%202021%20391-422.pdf https://doi.org/10.32890/jict2021.20.3.5 https://repo.uum.edu.my/id/eprint/28780/ https://e-journal.uum.edu.my/index.php/jict/article/view/14385 https://doi.org/10.32890/jict2021.20.3.5 |
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