Comparing performances of logistic regression and neural networks for predicting melatonin excretion patterns in the rat exposed to ELF magnetic fields

Abstract

Various studies have been reported on the bioeffects of magnetic field exposure; however, no consensus or guideline is available for experimental designs relating to exposure conditions as yet. In this study, logistic regression (LR) and artificial neural networks (ANNs) were used in order to analyze and predict the melatonin excretion patterns in the rat exposed to extremely low frequency magnetic fields (ELF-MF). Subsequently, on a database containing 33 experiments, performances of LR and ANNs were compared through resubstitution and jackknife tests. Predictor variables were more effective parameters and included frequency, polarization, exposure duration, and strength of magnetic fields. Also, five performance measures including accuracy, sensitivity, specificity, Matthew's Correlation Coefficient (MCC) and normalized percentage, better than random (S) were used to evaluate the performance of models. The LR as a conventional model obtained poor prediction performance. Nonetheless, LR distinguished the duration of magnetic fields as a statistically significant parameter. Also, horizontal polarization of magnetic fields with the highest logit coefficient (or parameter estimate) with negative sign was found to be the strongest indicator for experimental designs relating to exposure conditions. This means that each experiment with horizontal polarization of magnetic fields has a higher probability to result in "not changed melatonin level" pattern. On the other hand, ANNs, a more powerful model which has not been introduced in predicting melatonin excretion patterns in the rat exposed to ELF-MF, showed high performance measure values and higher reliability, especially obtaining 0.55 value of MCC through jackknife tests. Obtained results showed that such predictor models are promising and may play a useful role in defining guidelines for experimental designs relating to exposure conditions. In conclusion, analysis of the bioelectromagnetic data could result in finding a relationship between electromagnetic fields and different biological processes.

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

“…Furthermore, there are studies with relatively small samples where data mining techniques, like SVM and Neural Networks have been used with high accuracy in classification problems [see e.g. [58,64-66]]. Equivalent or even superior performances have been reported for Linear Discriminant Analysis and Random Forests when compared with Neural Networks, Classification Trees and Support Vector Machines [see e.g.…”

Health effects of extremely low‐frequency magnetic fields: reconsidering the melatonin hypothesis in the light of current data on magnetoreception

“…A significant correlation could be observed between ELF MF polarization and the nature of the findings (positive vs. negative), the lowest effective intensity (1.4 μT) being reported for circularly polarized fields (cf. Jahandideh et al ., ). Two studies that compared rats of different ages found an effect that was more pronounced in younger than in older rats (Selmaoui and Touitou, , ).…”

“…In rats, 22 studies were short term (≤ 2 weeks), 18 of them having been negative (review in Jahandideh et al, 2010;Swerdlow et al, 2006). And out of the seven studies that examined exposure of ≥ 4 weeks of duration to intensities > 1 mT, six have reported positive findings.…”

“…Interestingly, the first and last parameters have been reported as influencing MLT disruption by ELF MF in rats (Jahandideh et al, 2010;Touitou, 1995, 1999). And, on basis of epidemiological observations in humans, the second parameter, i.e.…”

Fifty hertz magnetic fields individually affect chromatin conformation in human lymphocytes: Dependence on amplitude, temperature, and initial chromatin state

“…Biochemical studies associated with ELF include effects on gene expression [Lupke et al, 2006], calcium signaling [Blackman et al, 1982;McCreary et al, 2006], enzymatic activity [Di Loreto et al, 2009;Ravera et al, 2010], growth factor receptors [Ke et al, 2008;Sun et al, 2008], melatonin synthesis [Jahandideh et al, 2010] and events involving generation of free radicals [Simko, 2007]. ELF effects on cell proliferation have been described and both inhibition and stimulation has been observed, depending on cell type and exposure conditions [Olsson et al, 2001;Strasak et al, 2002;Verheyen et al, 2003].…”