Machine learning for determination of the native background EPR signal amplitude in the teeth enamel

Abstract

The measurement of teeth in vivo (i.e., in the mouth, without extraction) with EPR spectroscopy in the L-band would allow to screen large groups of population in an event of an acute radiation exposure and in routine epidemiological studies. The radiation dose is proportional to intensity of the radiation-induced signal amplitude determined after subtraction of both native and solar light induced signal amplitudes from the total signal amplitude measured in L-band. Therefore, to improve the dose assessments of in vivo tooth dosimetry a better accuracy of native background signal is necessary. In this work, we present a search for the optimal machine learning approach for predicting of intensity of the native signal amplitude.The study used the dataset from two institutes composed of 1800 EPR spectra which were recorded in the X-band at a large-scale examination of the population of the Central Russia and North Kazakhstan. To determine the relevance of 12 various features a preliminary statistical significance analysis was used. Predictive models for native signal amplitude determination were developed and trained using standard Python frameworks for machine learning and data processing. The employed algorithms included 8 most popular machine learning regressors. To tune the performance of each algorithm a common evaluation technique, namely cross-validation, was used. Finally, mean squared error and coefficient of determination were calculated for performance analysis of the employed models.Comparison among the performance of all established prediction models revealed that Random Forest and Gradient Boosting had most superior performance. Overall, the application of machine learning methods was shown to provide a minor (5–11% in terms of R2) but non negligible improvement to the accuracy of native signal amplitude prediction. Using the technique of adding synthetic noise variables, the most significant factor regarding the prediction was position of tooth in the quadrant.