基于组合平衡算法的糖尿病预测方法的优化
HuiZhi Shao1,2, Xiang Liu2, DaShuai Zong2
1Jinan Engineering Polytechnic, Ji-Nan, Shandong, China.
Nutrition & diabetes
|August 14, 2024
概括
这项研究通过使用合成少数人过量采样技术 (SMOTE) 和随机不足采样技术 (RUS) 平衡不平衡的数据集来提高糖尿病预测. 优化的LightGBM模型显示了早期糖尿病检测的提高准确性和精度.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 医疗信息学 医疗信息学
背景情况:
- 糖尿病是一种主要的公共卫生问题,需要早期检测.
- 不平衡的数据集阻碍了准确的糖尿病预测模型性能,特别是在少数群体中.
研究的目的:
- 通过数据平衡和超参数优化,提高糖尿病预测准确度和模型效率.
- 解决当前研究中关于预测模型中不平衡数据处理的局限性.
主要方法:
- 应用合成少数群体过量采样技术 (SMOTE) 和随机不足采样 (RUS) 用于不平衡的糖尿病数据集平衡.
- 使用Optuna进行LightGBM机器学习模型的超参数优化.
- 通过比较数据平衡前后的模型性能来评估方法的有效性.
主要成果:
- 优化的LightGBM-Optuna模型实现了从97.07%到97.11%的轻微精度增加.
- 精度从97.17%大幅提高到98.99%的建议方法.
- 使用Optuna进行超参数优化,每次搜索只需要2.5秒,证明了计算效率.
结论:
- 将SMOTE和RUS与Optuna结合起来,有效地增强了不平衡糖尿病数据集的机器学习模型.
- 提出的方法提高了糖尿病预测任务的预测性能和效率.
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