Stabilizing machine learning for reproducible and explainable results: A novel validation approach to subject-specific insights

Subject-specific models improve generalization by addressing variability in human biology but are often costly and impractical for clinical trials. In this study, we introduce a novel validation technique for determining both group- and subject-specific feature importance within a general machine learning model, achieving greater stability in feature selection, higher predictive accuracy, and improved model interpretability. Our proposed approach ensures reproducible accuracy metrics and reliable feature rankings when using models incorporating stochastic processes, making machine learning models more robust and clinically applicable.