用机器学习建模青少年白天嗜睡的特征贡献和预测准确性:MeLiSA研究
Mohammed A Mamun1,2,3, Jannatul Mawa Misti1, Md Emran Hasan1,4
1CHINTA Research Bangladesh, Dhaka 1342, Bangladesh.
Brain sciences
|October 25, 2024
概括
过度的白天嗜睡 (EDS) 影响了孟加拉国11.6%的青少年. 机器学习模型将自我评估的健康,性别和抑郁症确定为关键风险因素,从而实现更好的预测和干预策略.
科学领域:
- 公共卫生 公共卫生
- 计算医学是一种计算医学.
- 青少年健康 青少年健康
背景情况:
- 青少年的过度白天嗜睡 (EDS) 与学业表现不佳,心理健康问题和幸福感降低有关.
- 了解孟加拉国等不同人群中EDS的流行率和风险因素对于有针对性的干预至关重要.
研究的目的:
- 确定孟加拉国青少年中EDS的患病率和风险因素.
- 应用机器学习 (ML) 模型来预测该群体中的EDS风险.
主要方法:
- 一项涉及1496名青少年的横截面研究,使用结构化问卷和两阶段分层集群抽样.
- 统计分析 (千平方,物流回归) 和ML模型 (CatBoost,XGBoost,SVM,RF,KNN,GBM) 用于风险因素的识别和预测.
- 用SHAP值来解释CatBoost模型中预测因素的意义.
主要成果:
- 在被调查的青少年中,EDS的患病率为11.6%.
- 根据SHAP值,自我评估的健康状况,性别和抑郁症成为EDS最重要的预测因素.
- 梯度提升机 (GBM) 和分类提升 (CatBoost) 模型表现出高精度 (分别为90.15%和89.48%) 和强大的预测性能 (AUC为0.86).
结论:
- 健康状况和人口因素显著预测孟加拉青少年的EDS.
- 机器学习模型有效地识别EDS风险因素,并为有针对性的干预提供卓越的预测能力.
- 建议进行进一步的纵向和干预研究,以提高概括性,并制定有效的EDS管理策略.
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