Latent class multi-label classification to identify subclasses of disease for improved prediction

Disease subtyping can assist the development of precision medicine but remains a challenge in data analysis by reason of the many different methods to group individuals depending on their data. However, identification of subclasses of disease will help to produce better models which are more specific to patients and will improve prediction and interpretation of underlying characteristics of disease. This paper presents a novel algorithm that integrates latent class models with supervised learning. The new algorithm uses latent class models to cluster patients within groups that results in improved classification as well as aiding the understanding of the dissimilarities of the discovered groups. The methods are tested on data from patients with Systemic Sclerosis (SSc), a rare potentially fatal condition. Results show that the 'Latent Class Multi-Label Classification Model' improves accuracy when compared with competitive similar methods.