Using Unsupervised Clustering to Identify Pregnancy Co-Morbidities

Absent a priori knowledge, unsupervised techniques identify meaningful clusters that can form the basis for subsequent analyses. This study explored the problem of inferring comorbidity-based profiles of complex diseases through unsupervised clustering methodologies. This study first considered the K-Modes algorithm, followed by, the self organizing map (SOM) technique to extract co-morbidity based clusters from a healthcare discharge dataset. After validation of general cluster composition for diabetes mellitus, co-morbidity based clusters were identified for pregnancy. The SOM technique was found to infer distinct clusterings of pregnancy ranging from normal birth to preterm birth, and potentially interesting comorbidities that could be validated by published literature The promising results suggest that the SOM technique is a valuable unsupervised clustering method for discovering co-morbidity based clusters