Artificial intelligence supported professional prediction of Physical Activity Counseling Practices Scale in health professionals with a machine learning algorithm

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Abstract
Objective: To evaluate whether machine learning algorithms can predict healthcare professionals’ occupations (physiotherapist, nurse, and dietitian) from PACPS (Physical Activity Counseling Practices Scale) item responses. Methods: We conducted a cross-sectional study in Konya (January–April 2025) with 242 participants. Five algorithms (Random Forest, K-Nearest Neighbors, Support Vector Machine, Decision Tree, and Naive Bayes) were trained and evaluated as follows: we first performed a stratified 70:30 split of the original dataset (train n = 126, test n = 116). Data augmentation was then applied only to the training set to address class imbalance, increasing it to n = 269, while the test set remained untouched (n = 116), preserving an effective ≈70:30 ratio. Performance was assessed on the independent test set (n = 116) using accuracy, precision, recall, and F1-score. Random Forest feature importance was examined to aid interpretability. Results: On the test set (n = 116), accuracies were 0.76 (Support Vector Machine), 0.82 (K-Nearest Neighbors), 0.71 (Naive Bayes), 0.75 (Random Forest), and 0.67 (Decision Tree). Random Forest identified PACPS12 as the most informative item for discrimination among occupations. Conclusion: PACPS responses contain distinctive patterns that enable moderate occupation prediction, with SVM and KNN yielding the best generalization in this small-sample setting. These results support the feasibility of combining a clinically grounded scale with machine learning methods, while underscoring the need for larger and externally validated datasets before clinical implementation.

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