在老年患者中使用机器学习方法预测基于营养不良的贫血
Mehmet Göl1, Cemal Aktürk2, Tarık Talan2
1Department of Physiology, Faculty of Medicine, Gaziantep Islam Science and Technology University, Gaziantep, Turkey.
Journal of evaluation in clinical practice
|September 23, 2024
概括
机器学习使用营养不良和活动数据准确地预测老年人的贫血,即使没有血液测试. 这有助于老年患者的早期诊断和治疗.
科学领域:
- 老年医学 老年医学
- 计算医学是一种计算医学.
- 营养科学 营养科学
背景情况:
- 老年人的贫血,通常是由于营养缺乏 (铁,叶酸,维生素B12),增加了发病和死亡的风险.
- 早期的贫血诊断和治疗对于改善老年患者的治疗结果至关重要.
- 这项研究的重点是预测门诊老年患者群体的贫血.
研究的目的:
- 用机器学习 (ML) 方法预测老年患者的贫血诊断.
- 为了评估ML模型的性能,有和没有血图数据.
- 为未来的老年贫血研究提供有价值的数据集.
主要方法:
- 使用ML对血液图,生物化学,营养不良和身体/认知活动得分进行贫血分类.
- ML算法的比较,包括J48和随机森林.
- 仅使用非血液测试属性 (营养不良,体力活动) 进行预测性表现分析.
主要成果:
- 在使用所有可用的数据时,J48算法实现了97.77%的准确性.
- 除了血液图数据外,随机森林算法只使用营养不良和体力活动得分,实现了85.39%的准确性.
- 该数据集包括438名老年患者的观察.
结论:
- 老年患者的贫血可以在不依赖血液图数据的情况下准确预测.
- 这项研究强调了ML在老年人非侵入性贫血预测方面的潜力.
- 共享的数据集有助于进一步研究ML方法和老年疾病预测.
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