Machine learning modeling for predicting adherence to physical activity guideline
Ju-Pil Choe, Seungbak Lee, Minsoo Kang
Abstract
This study aims to create predictive models for PA guidelines by using ML and examine the critical determinants influencing adherence to the PA guidelines. 11,638 entries from the National Health and Nutrition Examination Survey were analyzed. Variables were categorized into demographic, anthropometric, and lifestyle categories. 18 prediction models were created by 6 ML algorithms and evaluated via accuracy, F1 score, and area under the curve (AUC). Additionally, we employed permutation feature importance (PFI) to assess the variable significance in each model. The decision tree using all variables emerged as the most effective method in the prediction for PA guidelines (accuracy = 0.705, F1 score = 0.819, and AUC = 0.542). Based on the PFI, sedentary behavior, age, gender, and educational status were the most important variables. These results highlight the possibilities of using data-driven methods with ML in PA research. Our analysis also identified crucial variables, providing valuable insights for targeted interventions aimed at enhancing individuals' adherence to PA guidelines.