Determining the critical success factors of oral cancer susceptibility prediction in Malaysia using fuzzy models
The aim of the study was to determine the success factors of oral cancer susceptibility prediction using fuzzy models. Three fuzzy prediction models including fuzzy logic, fuzzy neural network and fuzzy linear regression models were constructed and applied to a Malaysian oral cancer data set for can...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Kebangsaan Malaysia
2012
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| Online Access: | http://journalarticle.ukm.my/4478/ http://journalarticle.ukm.my/4478/1/16%2520Rosma.pdf |
| _version_ | 1848810531063857152 |
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| author | Rosma Mohd Dom, Basir Abidin, Sameem Abdul Kareem, Siti Mazlipah Ismail, Norzaidi Mohd Daud, |
| author_facet | Rosma Mohd Dom, Basir Abidin, Sameem Abdul Kareem, Siti Mazlipah Ismail, Norzaidi Mohd Daud, |
| author_sort | Rosma Mohd Dom, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | The aim of the study was to determine the success factors of oral cancer susceptibility prediction using fuzzy models. Three fuzzy prediction models including fuzzy logic, fuzzy neural network and fuzzy linear regression models were constructed and applied to a Malaysian oral cancer data set for cancer susceptibility prediction. The three models’ prediction performances were evaluated and compared. All the three fuzzy models were found to have 64% prediction accuracies for 1-input and 2-input predictor sets. However, when the number of input predictor set was increased to 3-input and 4-input, both fuzzy neural networks’ and fuzzy linear regression’s prediction accuracies increased to 80%, while fuzzy logic prediction accuracy remains at 64%. Fuzzy linear regression model was found to have the capability of quantifying the relationships between input predictors and the predicted outcomes and also suitable for small sample size. Fuzzy neural network model on the other hand, handles ambiguous relationship between variables well but lacks the ability to describe input-output association. The third model, fuzzy logic, is easy to construct but highly dependent on human expert-input. The outcome of this study is a computer-based prediction tool which can be used in cancer screening programs. |
| first_indexed | 2025-11-14T23:31:59Z |
| format | Article |
| id | oai:generic.eprints.org:4478 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T23:31:59Z |
| publishDate | 2012 |
| publisher | Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:44782016-12-14T06:36:08Z http://journalarticle.ukm.my/4478/ Determining the critical success factors of oral cancer susceptibility prediction in Malaysia using fuzzy models Rosma Mohd Dom, Basir Abidin, Sameem Abdul Kareem, Siti Mazlipah Ismail, Norzaidi Mohd Daud, The aim of the study was to determine the success factors of oral cancer susceptibility prediction using fuzzy models. Three fuzzy prediction models including fuzzy logic, fuzzy neural network and fuzzy linear regression models were constructed and applied to a Malaysian oral cancer data set for cancer susceptibility prediction. The three models’ prediction performances were evaluated and compared. All the three fuzzy models were found to have 64% prediction accuracies for 1-input and 2-input predictor sets. However, when the number of input predictor set was increased to 3-input and 4-input, both fuzzy neural networks’ and fuzzy linear regression’s prediction accuracies increased to 80%, while fuzzy logic prediction accuracy remains at 64%. Fuzzy linear regression model was found to have the capability of quantifying the relationships between input predictors and the predicted outcomes and also suitable for small sample size. Fuzzy neural network model on the other hand, handles ambiguous relationship between variables well but lacks the ability to describe input-output association. The third model, fuzzy logic, is easy to construct but highly dependent on human expert-input. The outcome of this study is a computer-based prediction tool which can be used in cancer screening programs. Universiti Kebangsaan Malaysia 2012-05 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/4478/1/16%2520Rosma.pdf Rosma Mohd Dom, and Basir Abidin, and Sameem Abdul Kareem, and Siti Mazlipah Ismail, and Norzaidi Mohd Daud, (2012) Determining the critical success factors of oral cancer susceptibility prediction in Malaysia using fuzzy models. Sains Malaysiana, 41 (5). pp. 633-640. ISSN 0126-6039 http://www.ukm.my/jsm/ |
| spellingShingle | Rosma Mohd Dom, Basir Abidin, Sameem Abdul Kareem, Siti Mazlipah Ismail, Norzaidi Mohd Daud, Determining the critical success factors of oral cancer susceptibility prediction in Malaysia using fuzzy models |
| title | Determining the critical success factors of oral cancer susceptibility prediction in Malaysia using fuzzy models |
| title_full | Determining the critical success factors of oral cancer susceptibility prediction in Malaysia using fuzzy models |
| title_fullStr | Determining the critical success factors of oral cancer susceptibility prediction in Malaysia using fuzzy models |
| title_full_unstemmed | Determining the critical success factors of oral cancer susceptibility prediction in Malaysia using fuzzy models |
| title_short | Determining the critical success factors of oral cancer susceptibility prediction in Malaysia using fuzzy models |
| title_sort | determining the critical success factors of oral cancer susceptibility prediction in malaysia using fuzzy models |
| url | http://journalarticle.ukm.my/4478/ http://journalarticle.ukm.my/4478/ http://journalarticle.ukm.my/4478/1/16%2520Rosma.pdf |