Multi-label classification and evidential approach in diseases diagnoses using physiological signals

Abstract

Cardiovascular diseases are the major cause of death in the world. In this paper, we propose an automatic diagnostic method for cardiovascular diseases based on physiological signals. The problem is treated as a multi-label classification problem, where a set of labels is associated to each cardiovascular disease. Physiological signals are processed to extract features from them, according to each label or set of labels. The multi-label classification problem is then transformed to a set of two-class classification problems, that is solved using the belief functions theory. Association rules are also used to improve the quality of the classification. The simulation on the online medical database MIMIC III gives a Hamming loss of about 25% with the proposed method without the association rules. The Hamming loss reaches then 21% with the whole contribution.