A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) using optimized ensemble learning

Abstract

Cancer is a collection of diseases that involves growing abnormal cells with the potential to invade or spread tothe body. Breast cancer is the second leading cause of cancer death among women. A method for 5-year breastcancer recurrence prediction is presented in this manuscript. Clinicopathologic characteristics of 579 breastcancer patients (recurrence prevalence of 19.3%) were analyzed and discriminative features were selectedusing statistical feature selection methods. They were further refined by Particle Swarm Optimization (PSO) asthe inputs of the classification system with ensemble learning (Bagged Decision Tree: BDT). The proper combi-nation of selected categorical features and also the weight (importance) of the selected interval-measurement-scale features were identified by the PSO algorithm. The performance of HPBCR (hybrid predictorof breast cancerrecurrence) was assessed using the holdout and 4-fold cross-validation. Three other classifiers namely as sup-ported vector machines, DT, and multilayer perceptron neural network were used for comparison. The selectedfeatures were diagnosis age, tumor size, lymph node involvement ratio, number of involved axillary lymphnodes, progesterone receptor expression,having hormone therapyand type of surgery.The minimum sensitivity,specificity, precision and accuracy of HPBCR were 77%, 93%, 95% and 85%, respectively in the entire cross-validation folds and the hold-out test fold. HPBCR outperformed the other tested classifiers. It showed excellentagreement with the gold standard (i.e. the oncologist opinion after blood tumor marker and imaging tests, andtissue biopsy). This algorithm is thus a promising online tool for the prediction of breast cancer recurrence