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慢性病中的机器学习技术:对分类模型性能进行比较研究.
Nguyen Dong Phuong1, Nguyen Trung Tuyen2, Vu Thi Thai Linh3
1CIRTech Institute, HUTECH University, Ho Chi Minh City, Vietnam.
Bioinformatics and biology insights
|July 30, 2025
概括
机器学习模型可以准确检测慢性病 (CKD). 这项研究使用Random Forest,XGBoost,SVM和用于早期CKD诊断的后勤回归实现了100%的准确性.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 慢性病 (CKD) 的患病率在各年龄段都在上升.
- 准确的CKD评估和监测对于预防损伤至关重要.
- 机器学习 (ML) 在医疗保健中为快速和精确的疾病检测提供了潜力.
研究的目的:
- 开发和评估一个ML系统来支持CKD诊断.
- 为了比较六个不同的ML算法的CKD检测性能.
主要方法:
- 使用UCL机器学习数据库来获取CKD数据.
- 通过赋值缺失值和应用多项式技术来增强特征来处理数据.
- 采用基于特征的分层划分与K-means集群.
- 实现并比较了随机森林,SVM,天真贝叶斯,物流回归,KNN和XGBoost算法.
主要成果:
- 随机森林,XGBoost,SVM和物流回归实现了100%的准确性.
- 纯粹的贝叶斯达到了97%的准确性,而KNN达到了93%的准确性.
- 开发的ML系统在CKD诊断中表现出高效率.
结论:
- 机器学习模型,特别是随机森林,XGBoost,SVM和后勤回归,显示出对准确的CKD诊断的特殊承诺.
- 这项研究强调了ML在提高早期发现和治疗CKD方面的有效性.
- 进一步整合这些系统可以显著帮助临床医生在患者护理.
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