机器学习框架用于预测对肥胖的易感性
Warda M Shaban1, Hossam El-Din Moustafa2, Mervat M El-Seddek3
1Communication and Electronics Engineering Department, Nile Higher Institute for Engineering and Technology, Mansoura, Egypt. warda_mohammed@nilehi.edu.eg.
Scientific reports
|October 8, 2025
概括
一个新的机器学习框架,ObeRisk,准确预测肥胖风险. 新型控制量子蝙蝠算法 (EC-QBA) 增强了特征选择,改善了早期检测和积极的健康管理.
科学领域:
- 计算生物学和生物信息学
- 机器学习在公共卫生中的应用.
背景情况:
- 肥胖是一个全球性的健康危机,是全球第五大死亡原因.
- 肥胖患病率的增加需要先进的方法来早期识别风险.
- 及时检测肥胖易感性使主动干预成为可能.
研究的目的:
- 介绍ObeRisk,一种用于预测肥胖风险的新型机器学习框架.
- 开发和评估用于特征选择的受控制的量子蝙蝠算法 (EC-QBA).
- 提高肥胖易感性预测模型的准确性和效率.
主要方法:
- 数据预处理包括处理零值,特征编码,异常值删除和规范化.
- 开发了一种新的特征选择方法,即受控制的量子蝙蝠算法 (EC-QBA).
- EC-QBA集成了用于参数控制的香农和用于本地搜索的量子机制.
- 使用各种机器学习算法 (LR,LGBM,XGB,AdaBoost,MLP,KNN,SVM) 进行选择的特征,最终预测由多数投票决定.
主要成果:
- 该EC-QBA特征选择方法在现有技术中表现出优异的性能.
- EC-QBA实现了96%的准确性,96%的精度,96.5%的灵敏度,96.25%的F测量.
- 结合EC-QBA的ObeRisk框架显著超过了现代肥胖预测策略.
结论:
- 拟议的ObeRisk框架与EC-QBA提供了一个高度准确和有效的方法来预测肥胖风险.
- 对于复杂的健康数据来说,EC-QBA在特征选择方法方面取得了重大进展.
- 这一框架有可能促进早期干预,并改善与肥胖相关的公共卫生结果.
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