Zhang Jie, Feng Xinyi, Wang Wenhe, Liu Shudan, Zhang Qin, Wu Di, Liu Qin
Research Center for Medicine and Social Development, School of Public Health, Chongqing Medical University, No. 1 Yixueyuan Road, Yuzhong District, Chongqing 400016, China.
College of Medical Informatics, Chongqing Medical University, No. 1 Yixueyuan Road, Yuzhong District, Chongqing 400016, China.
Behav Sci (Basel). 2024 Oct 15;14(10):947. doi: 10.3390/bs14100947.
BACKGROUND: Loneliness is increasingly emerging as a significant public health problem in children and adolescents. Predicting loneliness and finding its risk factors in children and adolescents is lacking and necessary, and would greatly help determine intervention actions. OBJECTIVE: This study aimed to find appropriate machine learning techniques to predict loneliness and its associated risk factors among schoolchildren. METHODS: The data were collected from an ongoing prospective puberty cohort that was established in Chongqing, Southwest China. This study used 822 subjects (46.84% boys, age range: 11-16) followed in 2019. Five models, (a) random forest, (b) extreme gradient boosting (XGBoost), (c) logistic regression, (d) neural network, and (e) support vector machine were applied to predict loneliness. A total of 39 indicators were collected and 28 predictors were finally included for prediction after data pre-processing, including demographic, parental relationship, mental health, pubertal development, behaviors, and environmental factors. Model performance was determined by accuracy and AUC. Additionally, random forest and XGBoost were applied to identify the important factors. The XGBoost algorithm with SHAP was also used to interpret the results of our ML model. RESULTS: All machine learning performed with favorable accuracy. Compared to random forest (AUC: 0.87 (95%CI: 0.80, 0.93)), logistic regression (AUC: 0.80 (95%CI: 0.70, 0.89)), neural network (AUC: 0.80 (95%CI: 0.71, 0.89)), and support vector machine (AUC: 0.79 (95%CI: 0.79, 0.89)), XGBoost algorithm had the highest AUC values 0.87 (95%CI: 0.80, 0.93) in the test set, although the difference was not significant between models. Peer communication, index of general affect, peer alienation, and internet addiction were the top four significant factors of loneliness in children and adolescents. CONCLUSIONS: The results of this study suggest that machine learning has considerable potential to predict loneliness in children. This may be valuable for the early identification and intervention of loneliness.
背景：孤独正日益成为儿童和青少年中一个重大的公共卫生问题。目前缺乏且有必要预测儿童和青少年的孤独感并找出其风险因素，这将极大地有助于确定干预措施。 目的：本研究旨在寻找合适的机器学习技术来预测学童的孤独感及其相关风险因素。 方法：数据来自中国西南部重庆正在进行的一项前瞻性青春期队列研究。本研究使用了2019年追踪的822名受试者（46.84%为男孩，年龄范围：11 - 16岁）。应用了五种模型，（a）随机森林，（b）极端梯度提升（XGBoost），（c）逻辑回归，（d）神经网络，以及（e）支持向量机来预测孤独感。总共收集了39项指标，经过数据预处理后最终纳入28个预测变量进行预测，包括人口统计学、亲子关系、心理健康、青春期发育、行为和环境因素。通过准确率和AUC来确定模型性能。此外，应用随机森林和XGBoost来识别重要因素。还使用了带有SHAP的XGBoost算法来解释我们机器学习模型的结果。 结果：所有机器学习的准确率都不错。与随机森林（AUC：0.87（95%CI：0.80，0.93））、逻辑回归（AUC：0.80（95%CI：0.70，0.89））、神经网络（AUC：0.80（95%CI：0.71，0.89））和支持向量机（AUC：0.79（95%CI：0.79，0.89））相比，XGBoost算法在测试集中的AUC值最高，为0.87（95%CI：0.80，0.93），尽管各模型之间差异不显著。同伴交流、总体情感指数、同伴疏离和网络成瘾是儿童和青少年孤独感的前四大重要因素。 结论：本研究结果表明，机器学习在预测儿童孤独感方面具有相当大的潜力。这对于孤独感的早期识别和干预可能具有重要价值。
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