一个使用先进机器学习方法进行预测的框架,用于测量和分析合成农业化学品对人类健康的影响
Sahezpreet Singh1, Puneet Kaur2, Inderdeep Kaur2
1Department of Computer Science, Guru Nanak Dev University, Amritsar, India. sahezpreetdcs@gndu.ac.in.
Scientific reports
|May 3, 2025
概括
合成农业化学品对健康构成风险,但机器学习模型可以准确预测死亡率. 像LightGBM-PSO这样的先进技术实现了98.87%的准确性,有助于公共卫生政策.
科学领域:
- 环境健康 环境健康
- 计算毒理学计算毒理学
- 公共卫生信息学 公共卫生信息学
背景情况:
- 合成农业化学品对于作物生产至关重要,但与严重的人类健康问题有关,包括神经系统疾病和癌症.
- 现有的研究突出了农药对健康的有害影响,但用于精确风险评估的机器学习应用仍然有限.
- 农业工人和弱势群体因接触农业化学品而面临不成比例的高健康风险.
研究的目的:
- 通过先进的机器学习 (ML) 技术,研究合成农用化学品对人类健康的影响.
- 开发一个强大的框架来评估农业化学品对健康的风险,并提高对死亡病例的预测准确性.
- 为风险评估策略提供新的见解,并为公共卫生政策提供信息.
主要方法:
- 利用多层次的特征选择 (相互信息获取,递归特征消除) 和混合集体学习 (随机森林,LightGBM,CatBoost).
- 采用先进的技术,包括SHAP,定制的损失函数来惩罚假负值,以及优化算法 (粒子群优化,遗传算法).
- 数据来源于知名组织 (WHO,CDC,EPA,NHANES,USDA) 并经过广泛的预处理.
主要成果:
- 组合模型在预测与农业化学品相关的健康风险方面表现出卓越的表现.
- 轻GBM-PSO + CustomLoss模型实现了最高的准确性 (98.87%),精度 (98.59%),回忆 (99.27%) 和F1得分 (98.91%).
- 定制损失功能有效地减少了错误分类,特别是错误阴性,提高了死亡率预测.
结论:
- 先进的机器学习为准确的农业化学健康风险评估提供了强大的框架.
- 开发的模型为改善公共安全和告知监管框架提供了重大潜力.
- 未来的研究应该集中在多区域数据集,外部验证和与公共卫生监测系统的整合上.
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