Clustering methods and Bayesian inference for the analysis of the evolution of immune disorders

Abstract

Choosing appropriate hyperparameters for unsupervised clustering algorithms could be an optimal way for the study of long-standing challenges with data, which we tackle while adapting clustering algorithms for immune disorder diagnoses. We compare the potential ability of unsupervised clustering algorithms to detect disease flares and remission periods through analysis of laboratory data from systemic lupus erythematosus (SLE) patients records with different hyperparameter choices. To determine which clustering strategy is the best one we resort to a Bayesian analysis based on the Plackett-Luce model applied to rankings. This analysis quantifies the uncertainty in the choice of clustering methods for a given problem.