HBagging-MCDM: an ensemble classifier combined with multiple criteria decision making for rectal cancer survival prediction

As a main type of colorectal cancer, rectal cancer has a high risk and mortality rate so it is very important to accurately predict the survivability of patients to make better decisions on medical treatment and preparation for medical expenses. In recent years, many scholars have studied the survivability of selected common cancers such as lung cancer using machine learning approaches. Therefore, this research proposes a heterogeneous ensemble classification model to predict the survivability of rectal cancer patients. The model employs four different types of classifiers as component classifiers and Bagging algorithm to generate example sets for training component classifiers. In the proposed model, heterogeneous ensemble can help improve the diversity of component classifiers and Bagging can lower the variance and enhance the stability of the model. Finally, a fuzzy multiple criteria decision making method named fuzzy TOPSIS is employed to fuse the results of component classifiers. We evaluated the proposed model on the rectal cancer patient records dataset extracted from Surveillance, Epidemiology, and End Results (SEER) database. The results show that the proposed model obtains a significant improvement in terms of four standard metrics, including accuracy, specificity, sensitivity and area under the receiver operating characteristic curve, compared with single component classifiers and some other state-of-the-art ensemble classification models, such as Random Forest and Gradient Boosting Tree. Experiments also show that fusing component classifiers with fuzzy TOPSIS is superior to voting and simple weighted average methods. The proposed model outperforms other techniques in rectal cancer survival prediction, thereby improving the prognosis of rectal cancer patients and further assisting clinicians in developing better treatment plans.