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Lessons learned from the application of machine learning to studies on plant response to radio-frequency

机译：从机器学习的应用中吸取的经验教训研究植物响应射频

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This paper applies Machine Learning (ML) algorithms to peer-reviewed publications in order to discern whether there are consistent biological impacts of exposure to non-thermal low power radio-frequency electromagnetic fields (RF-EMF). Expanding on previous analysis that identified sensitive plant species, we extracted data from 45 articles published between 1996 and 2016 that included 169 experimental case studies of plant response to RF-EMF. Raw-data from these case studies included six different attributes: frequency, specific absorption rate (SAR), power flux density, electric field strength, exposure time and plant type (species). This dataset has been tested with two different classification algorithms: k-Nearest Neighbor (kNN) and Random Forest (RF). The outputs are estimated using k-fold cross-validation method to identify and compare classifier mean accuracy and computation time. We also developed an optimization technique to distinguish the trade-off between prediction accuracy and computation time based on the classification algorithm. Our analysis illustrates kNN (91.17%) and RF (89.41%) perform similarly in terms of mean accuracy, nonetheless, kNN takes less computation time (3.38 s) to train a model compared to RF (248.12 s). Very strong correlations were observed between SAR and frequency, and SAR with power flux density and electric field strength. Despite the low sample size (169 reported experimental case studies), that limits statistical power, nevertheless, this analysis indicates that ML algorithms applied to bioelectromagnetics literature predict impacts of key plant health parameters from specific RF-EMF exposures. This paper addresses both questions of the methodological importance and relative value of different methods of ML and the specific finding of impacts of RF-EMF on specific measures of plant growth and health. Recognizing the importance of standardizing nomenclature for EMF-RF, we conclude that Machine Learning provides innovative and efficient RF-EMF exposure prediction tools, and we propose future applications in occupational and environmental epidemiology and public health.
机译：本文将机器学习（ML）算法应用于同行评审的出版物，以辨别是否存在与非热低功率射频电磁场（RF-EMF）暴露的一致生物学冲击。在先前的分析中扩展，鉴定敏感植物物种，我们从1996年至2016年间公布的45篇文章中提取了数据，其中包括对RF-EMF的植物反应的169例实验案例研究。来自这些案例研究的原始数据包括六种不同的属性：频率，特定吸收率（SAR），电量密度，电场强度，暴露时间和植物类型（物种）。此数据集已通过两个不同的分类算法进行测试：K-CORMALT邻居（KNN）和随机林（RF）。使用k折叠交叉验证方法估计输出以识别和比较分类器意味着精度和计算时间。我们还开发了一种优化技术，以基于分类算法区分预测精度与计算时间之间的权衡。我们的分析说明了KNN（91.17％）和RF（89.41％）在平均精度方面表现，仍然需要较少的计算时间（3.38秒）与RF（248.12 s）相比训练模型。在SAR和频率之间观察到非常强烈的相关性，以及功率通量密度和电场强度的SAR。尽管样品尺寸低（169例报告的实验案例研究），但是，这限制了统计功率，因此该分析表明，应用于生物电磁文献的ML算法预测来自特定RF-EMF暴露的关键植物健康参数的影响。本文涉及不同方法的方法论重要性和相对价值的问题，以及RF-EMF对植物生长和健康的具体措施的特定发现的特定发现。认识到标准化命名的重要性为EMF-RF，我们得出结论，机器学习提供了创新和高效的RF-EMF曝光预测工具，我们提出了职业和环境流行病学和公共卫生的未来应用。

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