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这是一个多类roc分析的插图,用于预测大学生之间的互联网成
Nishat Tasnim Thity1, Atikur Rahman1, Adisha Dulmini2
1Department of Statistics and Data Science, Jahangirnagar University, Dhaka, Bangladesh.
PloS one
|July 21, 2025
概括
互联网成 (IA) 影响大学生,其中3.77%的人表现出严重的症状. 关键预测因素包括背景,心理健康和COVID-19状态,随机森林模型显示预测的希望.
科学领域:
- 心理学 心理学 心理学
- 公共卫生 公共卫生
- 计算机科学 计算机科学
背景情况:
- 互联网成 (IA) 是一个日益严重的全球公共卫生问题,特别影响大学生.
- 了解IA的多方面的预测因素对于开发有效的干预措施至关重要.
研究的目的:
- 在孟加拉国大学生中确定四个严重程度的互联网成 (IA) 的显著预测因素.
- 评估各种机器学习 (ML) 模型在预测IA严重性的有效性.
主要方法:
- 一项涉及孟加拉国424名大学生的横截面调查.
- 使用Boruta算法进行特征选择,并使用ML模型 (DT,RF,SVM,LR) 进行多类分类.
- 使用混矩阵参数,ROC曲线和k倍交叉验证来评估性能.
主要成果:
- 严重IA的患病率为3.77%.
- 发现的重要预测因素包括大学生背景,抑郁,焦虑,压力,体力活动,家庭互动,记忆力丧失和COVID-19阳性.
- 与其他ML技术相比,随机森林 (RF) 模型显示出更高的预测精度 (精度=0.531,微平均AUC=0.7798).
结论:
- 机器学习框架可以准确预测像AI这样的行为成,并识别关键风险因素.
- 调查结果可以为决策者,教育工作者和家庭提供信息,以制定针对性战略,以预防AI和在大学中支持心理健康.
- 提高学生和家长对IA预测因素的认识,对于早期干预和缓解至关重要.
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