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用先进的优化方法通过口服摄入预测地下水健康概率风险,使用先进的优化方法

Fahad Jibrin Abdu¹, Sani I Abba², Jamilu Usman³

¹SDAIA-KFUPM Joint Research Center for Artificial Intelligence (JRCAI), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia.

Journal of contaminant hydrology

|July 15, 2025

概括

评估摄入地下水导致的癌症风险至关重要. 真实世界的数据对于准确的预测至关重要,因为人工智能生成的合成数据对于可靠的健康风险评估是不够的.

科学领域:

环境健康环境健康
数据科学数据科学数据科学
计算生物学计算生物学

背景情况:

在许多地区,地下水是饮用和农业的主要来源.
评估口服GW摄入的癌症风险对公共卫生至关重要.
生成型人工智能为数据增强提供了潜力,但需要验证.

研究的目的:

评估生成AI在产生癌症风险预测科学数据方面的可靠性.
用真实与合成GW数据比较各种机器学习模型的预测性能.
确定最优的模型来预测摄入GW导致的癌症风险.

主要方法:

集成真实实验数据与生成人工智能驱动的合成数据.
评估的独立模型:人工神经网络 (ANN),高斯过程回归 (GPR),支持向量机 (SVM) 和增强树 (BT).
利用贝叶斯优化 (BO) 来提高模型性能,并比较真实和合成数据集的结果.

主要成果:

在真实数据上,ANN和ANN-BO模型表现出高性能,误差低 (例如,ANN-BO测试MAE=0.0902).
在合成数据上训练和测试的模型表现出明显更高的错误 (例如,ANN-BO测试MAE=15.718),表明概括不佳.
贝叶斯优化提高了模型性能,特别是在真实数据上,但无法克服合成数据的局限性.

结论:

现实世界的实验数据对于准确预测 GW 摄入的癌症风险是不可或缺的.
生成型人工智能产生的合成数据虽然对某些应用有用,但目前还不足以进行可靠的健康风险评估.
使用现实世界的证据进行数据质量和验证对于最小化错误和确保环境健康预测模型可信度至关重要.

关键词:

地下水污染污染地下水污染危害健康的风险健康风险重金属是一种重金属.机器学习是机器学习.优化优化优化优化癌症风险的概率 (PCR)

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