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 (Pushpa Publishing House, 2018)
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,