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Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
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Applying Machine Learning to Determine the Behavioral Characteristics of Rodents with Traumatic Brain Injury in an Eight-arm Maze [PDF]

Shu-Cing Wu, Chi-Yuan Lin, Liang-Jyun Hong, and Chi-Chun Chen

(Received November 4, 2021; Accepted January 20, 2022)

Keywords: cognitive parameters, eight-arm maze, machine learning, traumatic brain injury, support vector machine, decision tree, random forest, k-nearest neighbor

In this study, we identified the cognitive parameters of rats with traumatic brain injury (TBI) in an eight-arm radial maze and used them for TBI classification through machine learning models. A total of 16 cognitive parameters were derived using a sensing trajectory bitmap in the eight-arm maze. Of these 16 parameters, five (i.e., short-term memory error, latency, total distance, frequency of movement from an arm without food to an arm with food, and frequency of entry into the arm on the right after exiting an arm) were selected as representative parameters and were input into four machine learning models, namely, support vector machine (SVM), decision tree, random forest, and k-nearest neighbor (KNN) models, to classify and compare sham rats and rats with TBI. The performance evaluation results for the machine learning models revealed that the SVM model had the best performance among the models. Its overall accuracy, sensitivity, and area under the receiver operating characteristic curve (AUC) were >85, 98, and >94%, respectively. At some postsurgical time points, the sensitivity and AUC of the SVM model even approached 100%. The random forest and KNN models had satisfactory performance on Day 28 postsurgery. Overall, the SVM model had satisfactory performance in classifying both mild and severe TBI. Our findings can serve as a reference for future research on TBI feature classification.

Corresponding author: Chi-Yuan Lin, Chi-Chun Chen




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