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

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 a

SUBMITTER: Cankaya M 

PROVIDER: S-EPMC12575953 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature