Machine learning modeling for predicting adherence to physical activity guideline.

IF 3.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Reports Pub Date : 2025-02-15 DOI:10.1038/s41598-025-90077-1

Ju-Pil Choe, Seungbak Lee, Minsoo Kang

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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.

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用于预测身体活动指南遵守情况的机器学习建模。

本研究旨在通过使用ML创建PA指南的预测模型，并检查影响遵守PA指南的关键决定因素。分析了来自全国健康和营养检查调查的11,638个条目。变量被分为人口统计学、人体测量学和生活方式三类。采用6 ML算法建立了18个预测模型，并通过准确性、F1评分和曲线下面积（AUC）进行评估。此外，我们采用排列特征重要性（PFI）来评估每个模型中的变量显著性。使用所有变量的决策树是预测PA指南最有效的方法（准确率= 0.705，F1得分= 0.819,AUC = 0.542）。根据PFI，久坐行为、年龄、性别和教育程度是最重要的变量。这些结果突出了在机器学习研究中使用数据驱动方法的可能性。我们的分析还确定了关键变量，为有针对性的干预措施提供了有价值的见解，旨在提高个人对PA指南的依从性。

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来源期刊

Scientific Reports Natural Science Disciplines-

CiteScore

7.50

自引率

4.30%

发文量

19567

审稿时长

3.9 months

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