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Artificial intelligence supported professional prediction of Physical Activity Counseling Practices Scale in health
Musa Çankaya1, Ahmet Arda Ersöz2, Şenay Burçin Alkan3
1Seydişehir Health Services, Vocational School Therapy and Rehabilitation Department, Necmettin Erbakan University, Konya, Turkey.
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.
