A Reliable Multi-classifier Multi-objective Model for Predicting Recurrence in Triple Negative Breast Cancer

Recurrence is a significant prognostic factor in patients with triple negative breast cancer, and the ability to accurately predict it is essential for treatment optimization. Machine learning is a preferred strategy for recurrence prediction. Most current predictive models are built based on single classifier and trained through a single objective. However, since many classifiers are available, selecting an optimal model is challenging. On the other hand, a single objective may not be a good measure to guide model training. We proposed a new multi-classifier multi-objective (MCMO) recurrence predictive model. Specifically, new similarity-based sensitivity and specificity were defined and considered as the two objective functions simultaneously during training. Also the evidential reasoning (ER) approach was used for fusing the output of each classifier to obtain more reliable outcome. Using the proposed MCMO model, we achieved a predictive area under the receiver operating characteristic curve (AUC) of 0.9 with balanced sensitivity and specificity. Furthermore, MCMO outperformed all the individual classifiers, and yielded more reliable results than other commonly used optimization and fusion methods.