Abstract
This study presents a data-driven machine learning approach to predict individual Galactic Cosmic Radiation (GCR) ion exposure for 4He, 16O, 28Si, 48Ti, or 56Fe up to 150 mGy, based on Attentional Set-shifting (ATSET) experimental tests. The ATSET assay consists of a series of cognitive performance tasks on irradiated male Wistar rats. The GCR ion doses represent the expected cumulative radiation astronauts may receive during a Mars mission on an individual ion basis. The primary objective is to synthesize and assess predictive models on a per-subject level through Machine Learning (ML) classifiers. The raw cognitive performance data from individual rodent subjects are used as features to train the models and to explore the capabilities of three different ML techniques for elucidating a range of correlations between received radiation on rodents and their performance outcomes. The analysis employs scores of selected input features and different normalization approaches which yield varying degrees of model performance. The current study shows that support vector machine, Gaussian naive Bayes, and random forest models are capable of predicting individual ion exposure using ATSET scores where corresponding Matthews correlation coefficients and F1 scores reflect model performance exceeding random chance. The study suggests a decremental effect on cognitive performance in rodents due to ≤150 mGy of single ion exposure, inasmuch as the models can discriminate between 0 mGy and any exposure level in the performance score feature space. A number of observations about the utility and limitations in specific normalization routines and evaluation scores are examined as well as best practices for ML with imbalanced datasets observed.
Highlights
Galactic Cosmic Radiation (GCR) is an inherent risk for crewed missions traveling beyond the magnetosphere encircling Earth (Pietsch et al, 2011; Delp et al, 2016)
This study finds that exposure to even 100 mGy 56Fe impairs cognitive performance in the Attentional Set-shifting (ATSET) and that Compound Discrimination (CD) is impaired across all doses
This paper explores the feasibility of using Machine Learning (ML) techniques to predict received radiation exposure on a rodent subject from their corresponding ATSET cognitive performance scores
Summary
Galactic Cosmic Radiation (GCR) is an inherent risk for crewed missions traveling beyond the magnetosphere encircling Earth (Pietsch et al, 2011; Delp et al, 2016). The effects that GCR has on human cognitive health performance remain an essential inquiry for deep. To quantify the potential change in human cognitive abilities to these levels of GCR ions, rodentbased human surrogate models are commonly used (Chancellor et al, 2018). Such studies show that the aggregate exposure to less than 250 mGy of various ions could have concomitant effects on cognitive performance for rodents (Parihar et al, 2015; Kiffer et al, 2019), implying potential complications for humans in space mission success
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