使用机器学习预测冠状病毒患者的阿尔茨海默氏症
Shahriar Mohammadi1, Soraya Zarei1, Hossain Jabbari2,3
1Information Technology Group, Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran.
Iranian journal of public health
|October 30, 2023
概括
在COVID-19患者中预测阿尔茨海默病至关重要. 随机森林算法表现出高精度,在识别面临COVID后认知衰退风险的个体方面表现优于其他算法.
科学领域:
- 神经学 神经学
- 传染性疾病 传染性疾病
- 人工智能的人工智能
背景情况:
- COVID-19 感染与患阿尔茨海默病的风险增加有关.
- 认知障碍,或"遗忘",是影响全球许多个人的重大COVID-19后并发症.
- 在COVID-19患者中早期预测阿尔茨海默病可以减轻神经系统后果的严重性.
研究的目的:
- 预测以前感染COVID-19的人群中阿尔茨海默病的发病情况.
- 在COVID-19的背景下,评估机器学习算法对阿尔茨海默病预测的有效性.
- 在 Nave Bayes,Random Forest 和 K-Nearest Neighbors (KNN) 中确定最准确的预测模型.
主要方法:
- 在2020年10月至2021年9月期间,从伊朗德黑兰省的COVID-19患者收集了一组数据.
- 使用了三个机器学习算法:Nave Bayes,随机森林和KNN.
- 模型性能使用精度,回忆,准确性和F1得分指标进行了定量评估.
主要成果:
- 无论是Nave Bayes还是Random Forest算法都实现了超过80%的预测准确度.
- 与Nave Bayes和KNN相比,随机森林算法显示出更高的预测准确性.
- 这项研究强调了机器学习在识别COVID-19后阿尔茨海默氏症风险方面的有效性.
结论:
- 随机森林算法是评估算法中最有效的模型,用于预测COVID-19患者的阿尔茨海默病.
- 这些发现为主动管理和潜在的预防提供了有价值的工具 阿尔茨海默病相关问题在个人从COVID-19恢复.
- 通过预测建模的早期识别可以显著改善患者的治疗结果,并减少认知能力下降的长期负担.
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