Application of Fuzzy Linear Regression with Symmetric Parameter for Predicting Tumor Size of Colorectal Cancer

Abstract

The colon and rectum is the final portion of the digestive tube in the human body. Colorectal cancer (CRC) occurs due to bacteria produced from undigested food in the body. However, factors and symptoms needed to predict tumor size of colorectal cancer are still ambiguous. The problem of using linear regression arises with the use of uncertain and imprecise data. Since the fuzzy set theory's concept can deal with data not to a precise point value (uncertainty data), this study applied the latest fuzzy linear regression to predict tumor size of CRC. Other than that, the parameter, error and explanation for the both models were included. Furthermore, secondary data of 180 colorectal cancer patients who received treatment in general hospital with twenty five independent variables with different combination of variable types were considered to find the best models to predict the tumor size of CRC. Two models; fuzzy linear regression (FLR) and fuzzy linear regression with symmetric parameter (FLRWSP) were compared to get the best model in predicting tumor size of colorectal cancer using two measurement statistical errors. FLRWSP was found to be the best model with least value of mean square error (MSE) and root mean square error (RMSE) followed by the methodology stated.

Estimation of parameters for two-stage mixed fuzzy linear regression models

“…In 1982, Tanaka et al [1] established the first fuzzy regression model. With the development, the performance of the improved model has been improved and applied in various fields [2][3][4] , but there are still deficiencies. For example, more observation data will lead to more fuzzy estimation of parameters [5] , and it is very sensitive to outliers [6] .…”