Prediction of melatonin excretion patterns in the rat exposed to ELF magnetic fields based on support vector machine and linear discriminant analysis

Bioeffects of magnetic field exposure have been motivated accomplishing various studies. However, no consensus or guideline is available for experimental designs relating exposure conditions as yet. In the present work, in order to analyze and predict the melatonin excretion patterns in the rat exposed to extremely low frequency magnetic fields (ELF-MF), linear discriminate analysis (LDA) and support vector machines (SVMs) were utilized. Subsequently, performances of LDA and SVMs were compared through resubstitution and jackknife tests on a database containing 33 experiments. Predictor variables were more effective parameters including 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 LDA as a conventional model obtained poor prediction performance. On the other hand, SVMs as a more powerful model, which has not been introduced in predicting melatonin excretion patterns in the rat exposed to ELF-MF, showed 0.38 value of MCC through jackknife test that confirms the higher reliability of the SVMs.