Deviation-support Based Fuzzy Ensemble Of Multi-modal Deep Learning Classifiers For Breast Cancer Prognosis Prediction

This study aimed to improve the accuracy of breast cancer survival prediction using a novel ensemble approach. The ensemble uses fuzzy integrals on support and deviation scores from base classifiers to calculate aggregated scores while considering how confident or uncertain each classifier is. The proposed ensemble mechanism was evaluated on a multi-modal breast cancer dataset of breast tumors collected from participants in the METABRIC trial. The proposed architecture proved its efficiency by achieving an accuracy, sensitivity, F1-score, and balanced accuracy of 82.88%, 58.64%, 62.94%, and 74.75% respectively, which are superior to the performance of individual classifiers and existing ensemble approaches.