Fuzzification of quantitative data to predict tumour size of colorectal cancer
Regression analysis has become more popular among researchers as a standard tool in analyzing data. This paper used fuzzy linear regression model (FLRM) to predict tumour size of colorectal cancer (CRC) data in Malaysia. 180 patients with colorectal cancer received treatment in hospital were recorde...
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| Format: | Article |
| Language: | en |
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Pushpa Publishing House
2018
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| Online Access: | http://eprints.uthm.edu.my/3471/1/AJ%202018%20%28349%29.pdf http://eprints.uthm.edu.my/3471/ http://dx.doi.org/10.17654/MS103050951 |
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| author | Shafi, Muhammad Ammar Rusiman, Mohd Saifullah Amir Hamzah, Nor Shamsidah Sufahani, Suliadi Firdaus Khamis, Azme Mohamad Azmi, Nur Azia Hazida |
| author_facet | Shafi, Muhammad Ammar Rusiman, Mohd Saifullah Amir Hamzah, Nor Shamsidah Sufahani, Suliadi Firdaus Khamis, Azme Mohamad Azmi, Nur Azia Hazida |
| author_sort | Shafi, Muhammad Ammar |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Regression analysis has become more popular among researchers as a standard tool in analyzing data. This paper used fuzzy linear regression model (FLRM) to predict tumour size of colorectal cancer (CRC) data in Malaysia. 180 patients with colorectal cancer received treatment in hospital were recorded by nurses and doctors. Based on the patient records, a triangular fuzzy data will be built toward the size of the tumour. Mean square error (MSE) and root mean square error (RMSE) will be measured as a part of the process for predicting the size of the tumour. The degree of fitting adjusted is set between 0 and 1 in order to find the least error. It was found that the combination of FLRM model with fuzzy data provided a better prediction compared to the FLRM model alone. Hence, this study concluded that the tumour size is directly proportional to several factors such as gender, ethnic, icd 10, TNM staging, diabetes mellitus, Crohn’s disease, |
| format | Article |
| id | my.uthm.eprints-3471 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2018 |
| publisher | Pushpa Publishing House |
| record_format | eprints |
| spelling | my.uthm.eprints-34712021-11-17T08:39:48Z http://eprints.uthm.edu.my/3471/ Fuzzification of quantitative data to predict tumour size of colorectal cancer Shafi, Muhammad Ammar Rusiman, Mohd Saifullah Amir Hamzah, Nor Shamsidah Sufahani, Suliadi Firdaus Khamis, Azme Mohamad Azmi, Nur Azia Hazida QA299.6-433 Analysis Regression analysis has become more popular among researchers as a standard tool in analyzing data. This paper used fuzzy linear regression model (FLRM) to predict tumour size of colorectal cancer (CRC) data in Malaysia. 180 patients with colorectal cancer received treatment in hospital were recorded by nurses and doctors. Based on the patient records, a triangular fuzzy data will be built toward the size of the tumour. Mean square error (MSE) and root mean square error (RMSE) will be measured as a part of the process for predicting the size of the tumour. The degree of fitting adjusted is set between 0 and 1 in order to find the least error. It was found that the combination of FLRM model with fuzzy data provided a better prediction compared to the FLRM model alone. Hence, this study concluded that the tumour size is directly proportional to several factors such as gender, ethnic, icd 10, TNM staging, diabetes mellitus, Crohn’s disease, Pushpa Publishing House 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/3471/1/AJ%202018%20%28349%29.pdf Shafi, Muhammad Ammar and Rusiman, Mohd Saifullah and Amir Hamzah, Nor Shamsidah and Sufahani, Suliadi Firdaus and Khamis, Azme and Mohamad Azmi, Nur Azia Hazida (2018) Fuzzification of quantitative data to predict tumour size of colorectal cancer. Far East Journal of Mathematical Sciences (FJMS), 103 (5). pp. 951-959. ISSN 0972-0871 http://dx.doi.org/10.17654/MS103050951 |
| spellingShingle | QA299.6-433 Analysis Shafi, Muhammad Ammar Rusiman, Mohd Saifullah Amir Hamzah, Nor Shamsidah Sufahani, Suliadi Firdaus Khamis, Azme Mohamad Azmi, Nur Azia Hazida Fuzzification of quantitative data to predict tumour size of colorectal cancer |
| title | Fuzzification of quantitative data to predict tumour size of colorectal cancer |
| title_full | Fuzzification of quantitative data to predict tumour size of colorectal cancer |
| title_fullStr | Fuzzification of quantitative data to predict tumour size of colorectal cancer |
| title_full_unstemmed | Fuzzification of quantitative data to predict tumour size of colorectal cancer |
| title_short | Fuzzification of quantitative data to predict tumour size of colorectal cancer |
| title_sort | fuzzification of quantitative data to predict tumour size of colorectal cancer |
| topic | QA299.6-433 Analysis |
| url | http://eprints.uthm.edu.my/3471/1/AJ%202018%20%28349%29.pdf http://eprints.uthm.edu.my/3471/ http://dx.doi.org/10.17654/MS103050951 |
| url_provider | http://eprints.uthm.edu.my/ |