机器学习的认知状态的分类在社区居住的肉女性:基于SHAP的身体活动和人类学因素的分析
Yasin Gormez1, Fatma Hilal Yagin2,3, Yalin Aygun4
1Department of Management Information Systems, Faculty of Economics and Administrative Sciences, Sivas Cumhuriyet University, Sivas 58140, Türkiye.
Medicina (Kaunas, Lithuania)
|October 29, 2025
概括
机器学习模型有效地使用微型心理状态检查 (MMSE) 评分对皮女性的认知状态进行了分类. 较高的体力活动与较低的认知衰退风险有关,这表明生活方式干预.
科学领域:
- 老年学是一门学科.
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
背景情况:
- 肌肉质量和功能丧失的萨尔科佩尼亚与认知能力下降有关.
- 了解这种联系对于老龄化人口至关重要.
研究的目的:
- 应用机器学习 (ML) 来分类社区居住的形女性的认知状态.
- 确定影响这一人口群体认知健康的关键因素.
主要方法:
- 分析了67名女性的数据,这些女性使用了迷你精神状态检查 (MMSE) 的得分.
- 评估了八个ML分类模型 (例如,CatBoost,AdaBoost) 具有超参数优化.
- 使用诸如加权F1分数,准确度和AUC等指标来评估性能.
主要成果:
- CatBoost获得了最高加权的F1分数和ROC-AUC.
- 在PR-AUC中,AdaBoost和梯度提升表现出色,这表明性能强.
- SHAP分析确定了适度的体力活动,步行频率和坐着时间作为关键预测因素.
结论:
- 基于提升的ML模型有效地识别了临床数据中的认知状态模式.
- 可修改的生活方式因素,特别是身体活动,在认知健康中起着重要作用.
- 机器学习和可解释的人工智能可以改善风险评估,并指导对认知衰退的干预.
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