Machine learning for individual epigenetic fingerprints as predictors of well-being in young adults

Abstract The crisis in youth mental health has intensified, especially after the COVID-19 pandemic. Traditional assessment tools like the Perceived Stress Scale and Highly Sensitive Person (HSP) index provide valuable insights. However, to address the multifaceted nature of mental issues, molecular biomarkers should be integrated with neuropsychological data when modeling these scales, in order to unravel the interplay of genetic, environmental, and psychological factors. This study explores the interaction of these factors using machine learning to model HSP scores in university students. By conducting exhaustive feature selection, a data-driven classification model is trained to provide individual multivariate fingerprints. Despite the limited sample size, the model achieves remarkable accuracy, sensitivity, and precision. The integration of epigenetic features seems crucial, indicating the importance of balancing neuropsychological and genetic influences for accurate modeling. Our findings pave the way for future clinical applications, since the collection of questionnaires and saliva samples might offer accessible avenues for mental health assessment and personalized healthcare.