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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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数据平衡方法对使用基于随机森林随机参数的非侵入性参数预测代谢综合征的影响

Sahar Mohseni-Takalloo1,2,3, Hadis Mohseni4, Hassan Mozaffari-Khosravi2,3

  • 1School of Public Health, Bam University of Medical Sciences, Bam, Iran.

BMC bioinformatics
|January 11, 2024
PubMed
概括

预测代谢综合征 (MetS) 对于识别糖尿病和心血管疾病的风险至关重要. 使用非侵入性数据的随机森林模型,增强了SplitBal平衡,显著提高了男性和女性的预测灵敏度.

关键词:
数据平衡的数据平衡.机器学习是机器学习.代谢综合征是代谢综合征.随机的森林随机的森林在SMOTE中使用.在斯普利特巴尔 (SplitBal) 的比赛中.

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