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使用特征选择算法和基于增强的机器学习分类器预测糖尿病

Fatima Rahman¹, Sheyum Hossain¹, Jun-Jiat Tiang²

¹Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.

Diagnostics (Basel, Switzerland)

|October 29, 2025

概括

这项研究引入了一种机器学习框架,用于准确预测糖尿病,通过优化的特征选择改进早期检测,并对不平衡的数据集增强算法.

科学领域:

医疗信息学医疗信息学
计算生物学计算生物学
医疗保健中的机器学习

背景情况:

糖尿病是一种主要的全球健康问题,需要及早诊断以预防并发症.
精确的糖尿病预测受到有限,杂和不平衡的数据集的阻碍.
现有的方法在特征选择,类不平衡和数据预处理方面面临挑战.

研究的目的:

为增强糖尿病预测开发一种新的机器学习框架.
为了应对特征选择,类不平衡和数据预处理的挑战.
提高早期糖尿病检测模型的准确性和可解释性.

主要方法:

对五种特征选择算法的系统评估:递归特征消除,灰狼优化器,粒子优化器,遗传算法和Boruta.
利用交叉验证和SHAP分析来进行特征选择和可解释性.
使用LightGBM (LGBM) 和XGBoost (XGBoost) 分类算法.

主要成果:

博鲁塔特征选择算法确定了前五个特征,通过LightGBM分类器实现了卓越的性能.
获得了85.16%的准确性和85.41%的F1分数.
与其他配置相比,训练时间减少了54.96%.

结论:

拟议的框架为早期发现糖尿病提供了强大而准确的解决方案.
在Pima印度糖尿病数据集和DiaHealth数据集上验证的有效性.
为早期发现糖尿病提供了一种具有成本效益,可解释性和临床相关性的方法.

关键词:

增强了分类器算法.糖尿病预测糖尿病预测特性选择算法 (FSAs) 是一种机器学习是机器学习.医学诊断医学诊断医学诊断

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