在孟加拉国慢性病患者中与失眠相关的患病率和因素:机器学习研究
Pronab Das1,2, Mohammad Arif1,3, Md Emran Hasan1,4
1CHINTA Research Bangladesh, Savar, Dhaka, 1342, Bangladesh.
Nature and science of sleep
|October 13, 2025
概括
失眠在孟加拉国慢性病患者中很普遍,受到各种因素的影响. 机器学习模型有效预测失眠风险,帮助早期发现和干预策略.
科学领域:
- 公共卫生 公共卫生
- 临床医学 临床医学
- 数据科学数据科学数据科学
背景情况:
- 失眠严重影响精神和身体健康,与慢性疾病有双向关系.
- 失眠的危险因素在一般人群中是已知的,但在孟加拉国慢性疾病患者中未得到充分研究.
- 机器学习 (ML) 在这个弱势群体中提供了改善失眠风险预测的潜力.
研究的目的:
- 确定孟加拉国成年慢性病患者中失眠的患病率和决定因素.
- 评估各种ML模型的预测性能,以识别失眠风险.
- 在这个人群中确定失眠的关键预测因素.
主要方法:
- 在达卡和Chattogram的1,222名慢性病患者的横截面研究.
- 使用失眠严重性指数 (ISI-7) 评估失眠.
- 六个ML分类器 (KNN,RF,SVM,GBM,XGBoost,CatBoost) 被训练和测试,使用SHAP和增益值进行特征重要性分析.
主要成果:
- 失眠的患病率为41.3%,与女性性别,联合家庭,城市居住,物质使用,小睡,晚期疾病发病,并发病症和未满足的精神卫生保健需求有关.
- 保护因素包括体力活动,充足的夜间睡眠,满足心理健康护理需求和尿路疾病的存在.
- CatBoost模型表现出卓越的性能 (准确率为71.67%,AUC为77.27%),精神保健需求的满足和夜间睡眠时间是最强的预测因素.
结论:
- 失眠在孟加拉国慢性病患者中很常见,与各种因素有关.
- 机器学习模型,特别是CatBoost,在这个人群中显示出早期失眠查的前景.
- 建议进行进一步的前性研究,以验证发现并为干预措施提供信息.
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