基于机器学习的增强多模型痴呆症检测使用数据丰富框架:利用维度的祝福
Khomkrit Yongcharoenchaiyasit1,2, Sujitra Arwatchananukul2, Georgi Hristov3
1Computer and Communication Engineering for Capacity Building Research Center, Chiang Rai 57100, Thailand.
Bioengineering (Basel, Switzerland)
|June 26, 2025
概括
这项研究通过使用机器学习和特征工程将其与心血管疾病区分开来,增强了痴呆症诊断. 丰富的数据集改善了所有模型,突出了临床预测中维度的价值.
科学领域:
- 计算医学和健康信息学.
- 机器学习在临床诊断中的应用.
背景情况:
- 早期痴呆症诊断对于老年人有效干预至关重要.
- 痴呆症通常与心血管疾病同时发生,使诊断复杂化.
- 现有的诊断方法可能很难有效地区分这些疾病.
研究的目的:
- 开发一个多类分类框架,以区分痴呆与心血管并发症 (心力衰竭,大动脉膜疾病).
- 为了提高预测性能和功能可访问性,利用"维度的祝福".
- 提高模型的通用性和对少数阶级的表现.
主要方法:
- 利用泰国医院26474份电子健康记录的数据集.
- 员工临床知情特征增强和边界合成少数人过量抽样技术 (SMOTE) 用于数据丰富和阶级不平衡.
- 在使用标准性能指标的原始和丰富数据集上评估多个机器学习模型 (例如,XGBoost,随机森林,TabNet).
主要成果:
- 所有评估的机器学习模型都在与原始数据相比,在丰富的数据集上显示出一致的性能改进.
- 功能增强和SMOTE有效地提高了模型通用性和少数类性能.
- 该研究证实了以领域专业知识为指导的增加维度对诊断准确性的好处.
结论:
- 拟议的框架有效地将痴呆与心血管疾病区分开来.
- 数据丰富策略显著提高了复杂临床诊断的机器学习模型的性能.
- 通过临床洞察利用维度为改善早期痴呆症检测提供了一个有希望的途径.
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