机器学习方法用于预测轻度认知障碍患者的阿尔茨海默病进展
Fatih Gelir1, Taymaz Akan1, Sait Alp2
1Department of Medicine, Louisiana State University Health Sciences Center at Shreveport, PO Box 33932, Shreveport, LA 71103-4228 USA.
Journal of medical and biological engineering
|March 6, 2025
概括
机器学习模型预测阿尔茨海默病 (AD) 从轻度认知障碍 (MCI) 的进展. 具有SHAP功能选择的XGBoost可提高早期AD检测的诊断准确度.
科学领域:
- 神经科学是一个神经科学.
- 生物医学信息学 生物医学信息学
- 机器学习 机器学习
背景情况:
- 阿尔茨海默病 (AD) 是一种神经退行性疾病,影响认知,记忆和行为.
- 轻度认知障碍 (MCI) 代表了AD之前的关键过渡阶段.
- 开发MCI转化为AD的准确预测模型对于及时干预至关重要.
研究的目的:
- 开发和评估用于预测MCI患者阿尔茨海默病进展的机器学习模型.
- 确定生物标志物和非生物标志物特征在预测疾病进展中的重要性.
- 加强患有AD风险的MCI患者的早期诊断和监测.
主要方法:
- 使用阿尔茨海默病神经成像计划 (ADNI) 数据库进行分析.
- 对比了八种分类方法和四种数据采样方法 (包括NEAR-MISS) 以实现数据集平衡和过度匹配的减少.
- 采用沙普利价值解释 (SHAP) 技术进行特征选择,以确定关键预测指标.
主要成果:
- XGBoost分类方法在预测MCI转换为AD方面表现出卓越的表现.
- 在数据集平衡方面,NEAR-MISS采样方法被证明是最有利的.
- SHAP分析有效地确定了关键的生物标志物和非生物标志物特征,提高了预测准确度.
结论:
- 选择特征的SHAP和XGBoost的组合为阿尔茨海默病诊断提供了更高的预测准确度.
- 这种方法有望改善MCI个体的早期检测和管理.
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