Abstract This cross-sectional study aimed to develop and preliminarily validate a machine learning model for preliminary forward head posture (FHP) screening and deploy the final model as an online tool. Among 825 university students, 625 comprised the development dataset (training set, n = 437; test set, n = 188), and 200 the exploratory external validation dataset. FHP was defined as a craniovertebral angle < 50°. Six machine learning models were compared through nested cross-validation.
