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Fuzzification of quantitative data to predict tumour size of colorectal cancer

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 09720871

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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, ulcerative colitis, polyp, history of cancer, endometrial, small bowel, hepatobiliary, urinary tract, ovarian, other cancer, colorectal, weight loss, diarrhea, blood stool and abdominal.

Item Type: Article
Uncontrolled Keywords: Fuzzy linear regression model (FLRM); fuzzy data; mean square error (MSE); root mean square error (RMSE)
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Applied Science and Technology > Department of Mathematics and Statistic
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 13 May 2019 01:12
Last Modified: 13 May 2019 01:12
URI: http://eprints.uthm.edu.my/id/eprint/11191
Statistic Details: View Download Statistic

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