Konya Technical University, Department of Electrical and Electronics Engineering, Konya, Republic of Turkey.
Karamanoğlu Mehmetbey University, Vocational School of Health Services, Karaman, Republic of Turkey.
J Hazard Mater. 2023 Aug 5;455:131616. doi: 10.1016/j.jhazmat.2023.131616. Epub 2023 May 10.
Toxic gases can be fatal as they damage many living tissues, especially the nervous and respiratory systems. They can cause permanent damage for many years by harming environmental tissue and living organisms. They can also cause mass deaths when used as chemical weapons. These chemical agents consist of organophosphates, namely ester, amide, or thiol derivatives of phosphorus, phosphonic or phosphinic acids, or can be synthesized independently. In this study, machine learning models were used to predict the toxicity of chemical gases. Toxic and non-toxic gases, consisting of 144 gases, were identified according to the United States Environmental Protection Agency, Occupational Safety and Health Administration, and the Centers for Disease Control and Prevention. Six machine-learning models were used to predict the toxicity of these chemical gases. The performance of the models was verified through internal and external validation. The results showed that the model's internal validation accuracy was 86.96% with the Relief-J48 algorithm. The accuracy value of the model was 89.65% with the Bayes Net algorithm for external validation. Our results reveal that identifying the toxicity of existing and potential chemicals is essential for the early detection of these chemicals in nature.
有毒气体可能是致命的，因为它们会损害许多活体组织，尤其是神经系统和呼吸系统。它们通过损害环境组织和生物，会在多年后造成永久性损害。当用作化学武器时，它们还可能导致大量死亡。这些化学制剂由有机磷化合物组成，即磷的酯、酰胺或硫醇衍生物、膦酸或次膦酸，或者可以独立合成。在这项研究中，我们使用机器学习模型来预测化学气体的毒性。根据美国环境保护署、职业安全与健康管理局和疾病控制与预防中心的标准，我们确定了有毒和无毒气体，这些气体由 144 种气体组成。我们使用了六种机器学习模型来预测这些化学气体的毒性。通过内部和外部验证来验证模型的性能。结果表明，使用 Relief-J48 算法的模型内部验证准确率为 86.96%，使用贝叶斯网络算法的模型外部验证准确率为 89.65%。我们的研究结果表明，识别现有和潜在化学物质的毒性对于在自然界中早期发现这些化学物质至关重要。
