Tao Hongjun, Wen Yang, Yu Rongfang, Xu Yining, Yu Fangliang
Department of Physical and Education, Anhui Jianzhu University, Hefei, China.
College of Sports Industry and Leisure, Nanjing Sport Institute, Nanjing, China.
Front Bioeng Biotechnol. 2025 May 30;13:1607419. doi: 10.3389/fbioe.2025.1607419. eCollection 2025.
Forward head posture frequently occurs among primary school children, potentially due to prolonged sedentary behavior associated with academic demands and reduced physical activity. However, existing prevention and screening methods fail to accurately and promptly predict the onset of forward head posture.
This study aims to identify highly sensitive predictive indicators for forward head posture in primary school children using the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm. Multiple machine learning algorithms are applied to construct distinct risk prediction models, with the most effective model selected through comparative analysis. The Shapley Additive Explanations (SHAP) method is used to quantify the influence of each feature on model outcomes, ensuring enhanced model interpretability.
Employing a cross-sectional study design, this research recruited 520 primary school-aged children, gathering data on demographics, anthropometrics, and physical activity levels. Univariate logistic regression was utilized to identify high-risk factors for forward head posture. The LASSO algorithm was subsequently applied to select key predictors. Six machine learning models-K-nearest neighbor (KNN), light gradient boosting machine (LGBM), extreme gradient boosting (XGBoost), random forest (RF), linear model (LM), and support vector machine (SVM)-were developed to predict risk. The performance of each model was evaluated, and the best-performing model was further interpreted using the Shapley Additive Explanations (SHAP) algorithm.
A total of 514 children were ultimately included in the study, of whom 300 exhibited forward head posture. LASSO analysis identified age, bodyweight, BMI, sex, and weekly total homework time as prominent risk indicators. Among the 6 predictive models, the random forest algorithm demonstrated the highest performance (AUC = 0.865), significantly outperforming the others. SHAP analysis revealed that BMI, bodyweight, and age were the most influential predictors, with BMI contributing the most.
The random forest-based prediction model achieved superior predictive accuracy for forward head posture among Chinese primary school children, emphasizing the importance of monitoring BMI, bodyweight, and age for early intervention and prevention efforts.
小学生中经常出现头部前倾姿势，这可能是由于与学业要求相关的久坐行为延长以及体育活动减少所致。然而，现有的预防和筛查方法无法准确、及时地预测头部前倾姿势的发生。
本研究旨在使用最小绝对收缩和选择算子（LASSO）回归算法，识别小学生头部前倾姿势的高敏感预测指标。应用多种机器学习算法构建不同的风险预测模型，并通过比较分析选择最有效的模型。使用夏普利值附加解释（SHAP）方法量化每个特征对模型结果的影响，以增强模型的可解释性。
本研究采用横断面研究设计，招募了520名小学适龄儿童，收集了人口统计学、人体测量学和身体活动水平的数据。单因素逻辑回归用于识别头部前倾姿势的高危因素。随后应用LASSO算法选择关键预测指标。开发了六个机器学习模型——K近邻（KNN）、轻梯度提升机（LGBM）、极端梯度提升（XGBoost）、随机森林（RF）、线性模型（LM）和支持向量机（SVM）——来预测风险。评估每个模型的性能，并使用夏普利值附加解释（SHAP）算法对表现最佳的模型进行进一步解释。
本研究最终纳入了514名儿童，其中300名表现出头部前倾姿势。LASSO分析确定年龄、体重、BMI、性别和每周总作业时间为突出的风险指标。在6个预测模型中，随机森林算法表现最佳（AUC = 0.865），显著优于其他模型。SHAP分析表明，BMI、体重和年龄是最具影响力的预测指标，其中BMI的贡献最大。
基于随机森林的预测模型在预测中国小学生头部前倾姿势方面具有卓越的准确性，强调了监测BMI、体重和年龄对于早期干预和预防工作的重要性。
