糖尿病早期预警和因子分析使用集体贝叶斯网络与SMOTE-ENN和Boruta
Xuchun Wang1, Jiahui Ren1, Hao Ren1
1Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China.
Scientific reports
|August 5, 2023
概括
检测早期糖尿病 (DM) 风险因素对于预防至关重要. 这项研究使用贝叶斯网络和数据重新抽样来识别家族史,年龄和肥胖等关键预测因素,改善DM风险预测.
科学领域:
- 公共卫生 公共卫生
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 糖尿病是全球主要的健康问题,在慢性非传染性疾病中排名第三.
- 早期发现DM风险因素对于有效的预防和管理策略至关重要.
- 现有的DM监测计划需要强大的方法来识别有风险的个人,特别是不平衡的数据集.
研究的目的:
- 构建贝叶斯网络 (BNs) 以检测DM的早期预警风险因素.
- 通过分析来自中国山西省的监测数据来预测DM风险.
- 通过对不平衡数据的特征查和重新抽样技术,加强DM风险因素的识别.
主要方法:
- 采用单变量分析和Boruta特征选择算法进行初始风险因素选.
- 使用SMOTE,边界SMOTE (BL-SMOTE) 和SMOTE-ENN重新抽样技术来解决DM数据中的类不平衡.
- 在处理的数据上使用 Tabu,Hill-climbing 和 MMHC 算法构建了 BNs,以识别显著的 DM 风险因素.
主要成果:
- 使用经过处理的数据的BN,DM分类的准确性得到了显著提高.
- 在SMOTE-ENN重新采样和BNs的组合中,在分类准确度方面表现出了最实质性的改进.
- 博鲁塔-SMOTE-ENN-Tabu模型确定了关键的DM风险因素:家族史,年龄,中心肥胖,高脂血症,盐分减少,职业,心率和BMI.
结论:
- 开发的贝叶斯网络模型,特别是与SMOTE-ENN等先进的重新采样技术相结合时,有效地改善了DM风险预测.
- 该研究成功地确定了糖尿病的关键风险因素,为公共卫生干预提供了宝贵的见解.
- 博鲁塔-SMOTE-ENN-Tabu模型为慢性疾病监测和在不平衡的数据集中早期发现风险因素提供了一种强大的方法.
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