Using Participant Similarity for the Classification of Epidemiological Data on Hepatic Steatosis

Clinical decision support relies on the findings of epidemiological (longitudinal and cross-sectional) studies on predictive features and risk factors for diseases. Such features flow into the diagnostic procedures. Personalized medicine, which aims to optimize clinical decision making by taking individual characteristics of the patients into account, relies on the findings of epidemiology on groups of cohort participants that have common risk factors and exhibit the outcome under study. The identification of such groups requires modeling and exploiting similarity among individuals described through medical tests. In this work, we study how similarity measures for complex objects contribute to class separation for a multifactorial disorder. We present a data preparation, partitioning and classification workflow on cohort participants for the disorder "hepatic steatosis", and report on our findings on classifier performance and identified important features.