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Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data.

ABSTRACT: Discerning behaviours of free-ranging animals allows for quantification of their activity budget, providing important insight into ecology. Over recent years, accelerometers have been used to unveil the cryptic lives of animals. The increased ability of accelerometers to store large quantities of high resolution data has prompted a need for automated behavioural classification. We assessed the performance of several machine learning (ML) classifiers to discern five behaviours performed by accelerometer-equipped juvenile lemon sharks (Negaprion brevirostris) at Bimini, Bahamas (25°44'N, 79°16'W). The sharks were observed to exhibit chafing, burst swimming, headshaking, resting and swimming in a semi-captive environment and these observations were used to ground-truth data for ML training and testing. ML methods included logistic regression, an artificial neural network, two random forest models, a gradient boosting model and a voting ensemble (VE) model, which combined the predictions of all other (base) models to improve classifier performance. The macro-averaged F-measure, an indicator of classifier performance, showed that the VE model improved overall classification (F-measure 0.88) above the strongest base learner model, gradient boosting (0.86). To test whether the VE model provided biologically meaningful results when applied to accelerometer data obtained from wild sharks, we investigated headshaking behaviour, as a proxy for prey capture, in relation to the variables: time of day, tidal phase and season. All variables were significant in predicting prey capture, with predations most likely to occur during early evening and less frequently during the dry season and high tides. These findings support previous hypotheses from sporadic visual observations.

SUBMITTER: Brewster LR

PROVIDER: S-EPMC5842499 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data.

Brewster L R LR Dale J J JJ Guttridge T L TL Gruber S H SH Hansell A C AC Elliott M M Cowx I G IG Whitney N M NM Gleiss A C AC

Marine biology 20180308 4

Discerning behaviours of free-ranging animals allows for quantification of their activity budget, providing important insight into ecology. Over recent years, accelerometers have been used to unveil the cryptic lives of animals. The increased ability of accelerometers to store large quantities of high resolution data has prompted a need for automated behavioural classification. We assessed the performance of several machine learning (ML) classifiers to discern five behaviours performed by accele ...[more]

PMID: 29563648

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Project description:BackgroundPrognosis, recurrence rate, and secondary prevention strategies differ by different etiologies in acute ischemic stroke. However, identifying its cause is challenging.ObjectiveThis study aimed to develop a model to identify the cause of stroke using machine learning (ML) methods and test its accuracy.MethodsWe retrospectively reviewed the data of patients who had determined etiology defined by the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) from CASE-II (NCT04487340) to train and evaluate six ML models, namely, Random Forests (RF), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), Ada Boosting, Gradient Boosting Machine (GBM), for the detection of cardioembolism (CE), large-artery atherosclerosis (LAA), and small-artery occlusion (SAO). Between October 2016 and April 2020, patients were enrolled consecutively for algorithm development (phase one). Between June 2020 and December 2020, patients were enrolled consecutively in a test set for algorithm test (phase two). Area under the curve (AUC), precision, recall, accuracy, and F1 score were calculated for the prediction model.ResultsFinally, a total of 18,209 patients were enrolled in phase one, including 13,590 patients (i.e., 6,089 CE, 4,539 LAA, and 2,962 SAO) in the model, and a total of 3,688 patients were enrolled in phase two, including 3,070 patients (i.e., 1,103 CE, 1,269 LAA, and 698 SAO) in the model. Among the six models, the best models were RF, XGBoost, and GBM, and we chose the RF model as our final model. Based on the test set, the AUC values of the RF model to predict CE, LAA, and SAO were 0.981 (95%CI, 0.978-0.986), 0.919 (95%CI, 0.911-0.928), and 0.918 (95%CI, 0.908-0.927), respectively. The most important items to identify CE, LAA, and SAO were atrial fibrillation and degree of stenosis of intracranial arteries.ConclusionThe proposed RF model could be a useful diagnostic tool to help neurologists categorize etiologies of stroke.Clinical trial registration[www.ClinicalTrials.gov], identifier [NCT01274117].

Dataset Information

Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data.

Publications

Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data.

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