Clustering-Based Hybrid Model for Predicting Symptoms for Colorectal Cancer: A Fuzzy Machine Learning Approach

Colorectal cancer (CRC) is a form of cancer that originates in the colon (large intestine) or rectum, which are components of the digestive system. It generally begins as small, benign growths known as polyps that can form on the inner lining of the colon or rectum. In Malaysia, colorectal cancer ranks among the most prevalent cancers, especially within the Chinese and Malay communities. A study released by the Malaysian National Cancer Registry indicates that colorectal cancer is the second most common cancer type following breast cancer, impacting both genders. The occurrence of colorectal cancer in Malaysia has been consistently increasing, with approximately 15–20% of cancer cases in the nation being colorectal cancer. This type of cancer arises when cells in the body start to multiply excessively, leading to various symptoms. In this research, a novel hybrid fuzzy linear regression with symmetric parameter clustering combined with a support vector machine (FLRWSPCSVM) model is employed to predict the high-risk symptoms associated with the onset of colorectal cancer in Malaysia. The study analyzed secondary data from 180 patients diagnosed with colorectal cancer and treated in a general hospital in Kuala Lumpur, considering twenty-five independent variables with various combinations of variable types. Furthermore, the model included parameters, errors, and explanations, along with two statistical measurement errors. The findings revealed that FLRWSPCSVM identified ovarian symptoms and a history of cancer symptoms as high-risk indicators for the development of colorectal cancer, with the lowest mean square error (MSE) recorded at 100.605 and a root mean square error (RMSE) of 10.030.