Reevaluating feature importances in machine learning models for schizophrenia and bipolar disorder: The need for true associations

Abstract

Skorobogatov et al. developed supervised machine learning models to predict diagnoses and illness states in schizophrenia and bipolar disorder. However, their reliance on bootstrap forests and generalized regressions introduces significant biases in feature importance assessments. This paper highlights the critical distinction between feature importances generated by machine learning and actual associations, which are often model-specific and context-dependent. We underscore the limitations of biased feature importances and advocate for the use of robust statistical methods, such as Chi-squared tests and Spearman’s correlation, to reveal true associations. Reassessing findings with these methods will enable more accurate interpretations and reinforce the importance of understanding the limitations inherent in machine learning methodologies.