Predicting rule-based dairy milk intake recommendations using machine learning: A robustness, calibration, and explainability study

Abstract

Personalized dairy guidance is clinically complex because nutritional needs, intolerance, allergy, bone health, and cardiometabolic factors can point toward different decisions. Machine learning can help study how structured variables map to recommendations, but performance on synthetic labels must not be confused with clinical validity. Five classifiers—Logistic Regression, Random Forest, LightGBM, K-Nearest Neighbors, and a soft-voting ensemble—were evaluated on the cited public synthetic dataset. An audit confirmed 10,000 records, 15 source predictors, one engineered age-group predictor, and a highly imbalanced three-class target: Maintain 7,981 (79.81%), Increase 2,002 (20.02%), and Reduce 17 (0.17%). The primary analysis used leakage-controlled stratified 10-fold cross-validation. Random Forest achieved the highest mean accuracy (0.9981 ± 0.0007) and macro ROC-AUC (1.0000 ± 0.0000), but its mean macro F1-score was 0.6661 ± 0.0005 and its geometric mean was 0 because it did not identify any of the 17 Reduce records in out-of-fold prediction. Logistic Regression achieved lower accuracy (0.9811 ± 0.0039) but the strongest balanced discrimination, with macro F1-score 0.8403 ± 0.0360, macro recall 0.9589 ± 0.0700, and geometric mean 0.9512 ± 0.0861. The overall paired difference in fold-level macro F1-score was significant (Friedman χ² = 26.88, p = 2.10 × 10− 5), although most differences among LightGBM, Logistic Regression, and the voting ensemble were not significant after Holm correction. The study provides a reproducible framework for interrogating machine learning behavior on rule-generated dairy recommendation labels. It does not establish a clinically validated recommendation system.