基于改进的灰狼优化算法-随机森林模型的儿童健康预测研究
1Department of Public Teaching, Hefei Preschool Education College, Hefei, China.
Medicine
|February 3, 2026
概括
一个新的混合模型 (IGWO-RF) 通过优化随机森林超参数,将儿科健康预测准确度提高到92.1%. 关键的健康决定因素包括BMI,运动和PM2.5暴露,为早期风险分层提供了潜在的可能性.
科学领域:
- 儿科健康 儿科健康
- 计算健康 计算健康
- 人工智能在医学中的应用
背景情况:
- 儿童健康对于公共卫生评估至关重要,但仍面临生活方式变化和环境因素带来的挑战,导致肥胖,过敏和呼吸系统问题增加.
- 传统的健康评估存在数据滞后和主观性问题,需要先进的预测模型.
- 儿科健康的复杂性需要创新的方法来准确和及时的风险评估.
研究的目的:
- 引入一种新的混合模型,即改进的灰狼优化随机森林 (IGWO-RF),用于增强儿科健康预测.
- 用儿童体检数据提高健康预测模型的准确性和可解释性.
- 通过先进的可解释AI技术,识别儿童健康的关键决定因素.
主要方法:
- 使用儿童体检数据开发了一个随机森林 (RF) 模型.
- 灰狼优化 (GWO) 算法得到了动态重量策略和精英保留机制 (IGWO) 的增强,以优化射频超参数.
- 沙普利增量解释 (SHAP) 值用于模型解释性和显著健康因素的识别.
主要成果:
- 该IGWO-RF模型实现了92.1%的预测准确度和90.8%的F1得分,超过了传统的RF (85.3%) 和PSO-RF (88.7%).
- SHAP分析确定了体重指数 (0.32),每日运动时间 (0.21) 和颗粒物2.5暴露 (0.18) 作为儿童健康的主要决定因素.
- 该模型在儿科健康风险分层方面表现出卓越的表现.
结论:
- IGWO-RF模型在儿科健康预测准确性和可解释性方面取得了重大进展.
- 影响儿童健康的关键因素,如BMI,运动和环境暴露,被定量确定.
- 拟议的方法框架对开发儿童健康风险和其他慢性疾病的早期预警系统充满希望.
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