开发基于机器学习的模型,用于预测和糖尿病病的进展:一个单一的以中心为中心的回顾性研究
Sandhya Nayak1, Ashwini Amin2, Swetha R Reghunath1
1Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Udupi District, Karnataka 576 104, India.
International journal of medical informatics
|July 14, 2024
概括
机器学习准确地预测了糖尿病病 (DKD) 的进展. 关键预测因素包括血清尿酸,尿素和,使得及时,个性化的患者护理.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 糖尿病学 糖尿病学
- 人工智能在医学中的应用
背景情况:
- 糖尿病病 (DKD) 是糖尿病的严重并发症,进展不可预测.
- 早期发现患有DKD进展风险的患者对于有效管理至关重要.
研究的目的:
- 开发一个机器学习模型来预测DKD阶段.
- 通过使用预测分析来确定导致DKD进展的关键因素.
主要方法:
- 印度南部227名DKD患者 (2017年1月至2022年1月) 的回顾性研究.
- 使用基于贝叶斯优化的机器学习 (分类和回归).
- 一个优化框架平衡了准确性,预测性和模型可解释性.
主要成果:
- 极端梯度 (XG) 提升模型实现了88.75%的精度,91.4%的灵敏度和100%的特异性.
- 确定了DKD进展的显著预测因素:血清尿酸,尿素,,红血细胞,和乙氨基酸细胞计数.
- 梯度提升回归器在回归任务中产生了0.97的R2得分.
结论:
- 机器学习模型可以有效地预测DKD阶段.
- 这便于为患有DKD的患者提供个性化和及时的医疗干预.
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