通过使用数据挖掘技术分析症状来预测糖尿病风险
Rahaf Alhamouri1, Ahmad Alaiad1, Dania Rahhal1
1Department of Computer Information Systems, Jordan University of Science and Technology, Irbid, Jordan.
Informatics for health & social care
|December 31, 2025
概括
这项研究表明,随机森林 (RF) 机器学习对于预测糖尿病风险非常有效. 射频可达到97%以上的准确性,使其成为早期疾病检测的宝贵工具.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 糖尿病是一种常见的慢性疾病,具有重大的个人和社会负担.
- 早期预测糖尿病风险对于及时干预和疾病管理至关重要.
- 机器学习为开发糖尿病风险预测模型提供了潜力.
研究的目的:
- 评估各种机器学习模型在预测糖尿病风险方面的效率.
- 确定早期糖尿病风险评估中最准确的算法.
- 探索计算方法在糖尿病预防中的应用.
主要方法:
- 采用了机器学习算法:决策树,天真贝叶斯,物流回归和随机森林 (RF).
- 利用了520个实例的数据集,其中16个与糖尿病风险症状相关的属性.
- 使用准确度,精度,回忆和F测量来评估性能,并进行了10倍的交叉验证和80:20数据分割.
主要成果:
- 随机森林 (RF) 显著优于其他算法,实现了97.5%的准确性,并进行了10倍的交叉验证.
- 射频显示了高性能指标,包括使用比率分割的95.2%准确度.
- 该模型实现了0.975的精度,回忆和F测量,表明了强大的预测能力.
结论:
- 随机森林 (RF) 被推为预测糖尿病风险的最佳模型,特别是在与10倍交叉验证相结合时.
- 该研究强调了机器学习在糖尿病风险评估临床决策中的潜力.
- 整合像RF这样的预测模型可以增强预防糖尿病的积极医疗保健策略.
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