Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.
Int J Environ Res Public Health. 2020 Jun 26;17(12):4595. doi: 10.3390/ijerph17124595.
The emergence of new technologies to incorporate and analyze data with high-performance computing has expanded our capability to accurately predict any incident. Supervised Machine learning (ML) can be utilized for a fast and consistent prediction, and to obtain the underlying pattern of the data better. We develop a prediction strategy, for the first time, using supervised ML to observe the possible impact of weak radiofrequency electromagnetic field (RF-EMF) on human and animal cells without performing in-vitro laboratory experiments. We extracted laboratory experimental data from 300 peer-reviewed scientific publications (1990-2015) describing 1127 experimental case studies of human and animal cells response to RF-EMF. We used domain knowledge, Principal Component Analysis (PCA), and the Chi-squared feature selection techniques to select six optimal features for computation and cost-efficiency. We then develop grouping or clustering strategies to allocate these selected features into five different laboratory experiment scenarios. The dataset has been tested with ten different classifiers, and the outputs are estimated using the k-fold cross-validation method. The assessment of a classifier's prediction performance is critical for assessing its suitability. Hence, a detailed comparison of the percentage of the model accuracy (PCC), Root Mean Squared Error (RMSE), precision, sensitivity (recall), 1 - specificity, Area under the ROC Curve (AUC), and precision-recall (PRC Area) for each classification method were observed. Our findings suggest that the Random Forest algorithm exceeds in all groups in terms of all performance measures and shows AUC = 0.903 where k-fold = 60. A robust correlation was observed in the specific absorption rate (SAR) with frequency and cumulative effect or exposure time with SAR×time (impact of accumulated SAR within the exposure time) of RF-EMF. In contrast, the relationship between frequency and exposure time was not significant. In future, with more experimental data, the sample size can be increased, leading to more accurate work.
新技术的出现使得我们能够利用高性能计算来整合和分析数据，从而更准确地预测任何事件。监督机器学习（ML）可用于快速、一致地进行预测，并更好地获取数据的潜在模式。我们首次开发了一种预测策略，利用监督 ML 来观察弱射频电磁场（RF-EMF）对人体和动物细胞可能产生的影响，而无需进行体外实验室实验。我们从 300 篇同行评议的科学出版物中提取了实验室实验数据（1990-2015 年），这些出版物描述了 1127 个人体和动物细胞对 RF-EMF 反应的实验案例研究。我们利用领域知识、主成分分析（PCA）和卡方特征选择技术，选择了六个用于计算和成本效益的最佳特征。然后，我们开发了分组或聚类策略，将这些选定的特征分配到五个不同的实验室实验场景中。该数据集已经使用了十种不同的分类器进行了测试，输出结果使用 k 折交叉验证方法进行估计。评估分类器的预测性能对于评估其适用性至关重要。因此，我们观察了每种分类方法的模型准确性百分比（PCC）、均方根误差（RMSE）、精度、灵敏度（召回率）、1-特异性、ROC 曲线下面积（AUC）和精度-召回率（PRC 面积）的详细比较。我们的研究结果表明，随机森林算法在所有组中都表现出色，在所有性能指标上均超过了其他算法，并且 AUC = 0.903，其中 k 折 = 60。RF-EMF 的比吸收率（SAR）与频率和累积效应或暴露时间与 SAR×时间（暴露时间内累积 SAR 的影响）之间存在很强的相关性。相比之下，频率与暴露时间之间的关系不显著。在未来，随着更多的实验数据，样本量可以增加，从而使工作更加准确。
