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使用机器学习模型预测记忆衰退风险:一个横截面研究.

Ying Song1, Yansun Sun2, Qi Weng1

  • 1Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, China.

Heliyon
|November 7, 2024
PubMed
概括

机器学习模型,特别是ExtraTrees分类器和XGBoost,在预测美国成年人的记忆力下降风险因素方面表现有前途. 这些模型为早期识别认知障碍提供了更高的准确性.

科学领域:

  • 神经科学是一个神经科学.
  • 医疗信息学 医疗信息学
  • 生物统计学 生物统计学

背景情况:

  • 记忆力下降是神经退行性疾病的早期指标,如阿尔茨海默病 (AD).
  • 预测和识别记忆衰退的危险因素在临床实践中仍然是一个重大挑战.
  • 早期检测对于及时干预和管理认知障碍至关重要.

研究的目的:

  • 开发和验证机器学习 (ML) 模型,用于预测美国成年人的记忆力下降风险因素.
  • 通过使用先进的分析技术,识别与记忆恶化相关的关键预测因素.
  • 提高认知衰退早期风险评估的准确性.

主要方法:

  • 在2015-2016年国家健康和营养检查调查 (NHANES) 中利用了9971人的数据.
  • 应用最少绝对收缩和选择运算符 (LASSO) 进行预测选.
  • 评估了五种ML算法:物流回归,ExtraTrees分类器,包装分类器,极端梯度提升 (XGBoost) 和随机森林 (RF).

主要成果:

  • 最终的样本包括4525名受试者,其中7.7%的受试者经历了记忆力恶化.
  • ExtraTrees分类器和XGBoost模型表现出优异的预测性能,曲线下面积 (AUC) 值分别为0.915和0.911.
关键词:
阿尔茨海默氏症的疾病是阿尔茨海默氏症.机器学习 机器学习记忆力下降 记忆力下降尼汉斯 (NHANES) 是一个名人.这就是 SHAP SHAP 的意思.

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  • 这些模型在外部数据集上显示出一致的准确性 (AUC 0.851 和 0.843).

    • 结论:

    • ExtraTrees分类器和XGBoost模型在预测记忆衰退方面非常有效.
    • 这些ML模型在识别有认知障碍风险的个体方面具有显著的临床价值.
    • 需要进一步的研究来验证这些发现,并探索临床实施.