Multi-label classification methods for improving comorbidities identification

The medical diagnostic process may be supported by computational classification techniques. In many cases, patients are affected by multiple illnesses, and more than one classification label is required to improve medical decision-making. In this paper, we consider a multi-perspective classification problem for medical diagnostics, where cases are described by labels from separate sets. We attempt to improve the identification of comorbidities using multi-label classification techniques. Several investigated methods, which provide label dependencies, are analysed and evaluated. The methods' performances are verified by experiments conducted on four sets of medical data from subject patients. The results were evaluated using several metrics and were statistically verified. We compare the effects of the techniques that do and do not consider label correlations. We demonstrate that multi-label classification methods from the first group outperform the techniques from the second one.