A prediction survival model based on support vector machine and extreme learning machine for colorectal cancer

Abstract

Colorectal cancer is the third largest cause of cancer deaths in men and second most common in women worldwide. In this paper, a prediction model based on Support Vector Machine (SVM) and Extreme Learning Machine (ELM) combined with feature selection has been developed to estimate colorectal-cancer-specific survival after 5 years of diagnosis. Experiments have been conducted on dataset of Colorectal Cancer patients publicly available from Surveillance, Epidemiology, and End Results (SEER) program. The performance measures used to evaluate proposed methods are classification accuracy, F-score, sensitivity, specificity, positive and negative predictive values and receiver operating characteristic (ROC) curves. The results show very good classification accuracy for 5-year survival prediction for the SVM and ELM model with 80%–20% partition of data with 16 number of features and this is very promising as compared to existing learning models result.