Best practices for supervised machine learning when examining biomarkers in clinical populations

Abstract

Machine learning approaches are increasingly used in health research. Applications range from the identification of disease onset, classification of disease severity, to predicting epileptic seizures. Although machine learning can be a powerful tool, there is potential for misuse; model performance can be inflated through overfitting and, consequently, will not generalize to the greater population. The risk of misuse increases when the number of variables extracted from continuous data is almost unlimited, as is the case for neural, movement, and acoustic (e.g., speech and music) data. Given that health research may contain small sample sizes, and outcome variables can be noisier for clinica..