使用树结构的Parzen估计器优化超参数,以改善糖尿病预测
Raafat M Munshi1, Lammar R Munshi2, Hanen Himdi3
1Department of Medical Laboratory Technology (MLT), Faculty of Applied Medical Sciences, King Abdulaziz University, Rabigh, Saudi Arabia. rmonshi@kau.edu.sa.
Scientific reports
|October 10, 2025
概括
机器学习,使用XGBoost,从实验室测试中改善糖尿病风险预测. 这种方法提高了识别高风险患者的准确性,以获得更好的健康结果.
科学领域:
- 内分泌学和代谢性疾病.
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 糖尿病是一种影响胰岛素生产的慢性疾病,需要及早识别高风险个体,以有效管理.
- 使用临床数据的传统风险预测模型在准确性和广泛适用性方面存在局限.
- 机器学习 (ML) 在糖尿病风险分层中提供了增强诊断策略的潜力.
研究的目的:
- 开发和验证基于机器学习的诊断策略,以改善高风险糖尿病患者的预测.
- 利用与Optuna优化的XGBoost算法,以提高使用实验室参数的预测精度.
- 将拟议的ML模型与传统分类方法的性能进行比较.
主要方法:
- 利用了包括患者人口统计,实验室结果和临床结果在内的开放式糖尿病数据集.
- 采用了数据预处理技术,包括使用自适应树结构帕森估计器 (ATPE) 和XGBoost.清理,规范化和特征提取.
- 实施和评估XGBoost算法用于高风险患者预测.
主要成果:
- 拟议的XGBoost模型实现了83%的准确性,80%的精度,78%的回忆,以及78%的F1得分.
- 该模型在区分高风险患者方面,与传统分类模型相比,表现优越.
- 相关性和混矩阵分析证实了该模型在患者分层中的有效性.
结论:
- 将基于ML的风险分类与实验室数据相结合,可以显著提高糖尿病的预测准确性和患者分层.
- 该研究强调了优化XGBoost在糖尿病风险评估中的临床决策方面的潜力.
- 未来的方向包括整合实时数据,并将ML应用扩展到其他疾病.
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