一个贡献可解释的信念规则基础模型,用于使用物理指标和饮食习惯来预测肥胖风险
Fanxu Wei1, Li Jiang2, Hailong Zhu1
1School of Computer Science and Information Engineering, Harbin Normal University, Harbin, China.
Frontiers in endocrinology
|December 24, 2025
概括
一个新的双层信念规则基础模型 (DBRB-C) 准确预测肥胖风险,识别关键的饮食因素. 这种可解释的方法有助于个性化干预和内分泌学中的临床决策.
科学领域:
- 内分泌学 在内分泌学.
- 预防性健康 预防性健康
- 计算生物学 计算生物学
背景情况:
- 肥胖是一种全球性内分泌疾病,与2型糖尿病,心血管疾病和代谢疾病有关.
- 准确和可解释的肥胖风险预测对于及时干预至关重要,但目前的模型往往缺乏.
研究的目的:
- 为多层次肥胖风险预测开发一种新型模型,以平衡准确性和可解释性.
- 通过透明的决策途径,识别肥胖的主要可修改的风险因素.
主要方法:
- 提出了一种双层信念规则基础模型,其中包括混合进化优化和贡献分析 (DBRB-C).
- 集成的数据驱动学习与基于知识的推理,使用层次架构.
- 采用差异进化-粒子群集优化用于参数学习和可追溯性的贡献分析.
主要成果:
- 与现实数据集上的传统机器学习模型相比,DBRB-C显示出更高的准确性和稳定性.
- 确定了特定的饮食行为,如蔬菜消费和食频率,作为显著的肥胖风险决定因素.
- 为每个预测提供了完全可追溯的推理,提高了模型的透明度.
结论:
- DBRB-C模型有效地解决了肥胖风险评估中的准确性-解释性权衡问题.
- 提供个性化生活方式干预和基于证据的临床决策在内分泌学的可靠框架.
- 强调了透明,贡献意识的模型在预防性健康中的潜力.
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