An interpretable machine learning model for precise prediction of biomarkers for intermittent fasting pattern.

Abstract

Intermittent fasting is currently a highly sought-after dietary pattern. To explore the potential biomarkers of intermittent fasting, untargeted metabolomics analysis of fecal metabolites in two groups of mice, intermittent fasting and normal feeding, was conducted using UPLC-HRMS. The data was further analyzed through interpretable machine learning (ML) to data mine the biomarkers for two dietary patterns. We developed five machine learning models and results showed that under three-fold cross-validation, Random Forest model was the most suitable for distinguishing the two dietary patterns. Finally, Shapely Additive exPlanations (SHAP) were explored to perform a weighted explanatory analysis on the Random Forest model, and the contribution of each metabolite to the model was calculated. Results indicated that Ganoderenic Acid C is the potential biomarkers to distinguish the two dietary patterns. Our work provides new insights for metabolic biomarker analysis and lays a theoretical foundation for the selection of a healthieir dietary lifestyle.