{"title":"Prediction of lamb survival using machine learning algorithms with neonatal lamb behaviors and maternal behavior score in Kivircik lambs","authors":"Bulent Ekiz, Hulya Yalcintan, Omur Kocak, Pembe Dilara Kecici","doi":"10.1016/j.jveb.2024.06.008","DOIUrl":null,"url":null,"abstract":"<div><p>The aims of this study were to examine the relationship between lamb and ewe behaviors in postnatal 3-hour and lamb survival using machine learning (ML) algorithms and to determine the best ML classifier to predict lamb survival. The research data consisted of postnatal 3-hour behavior records of 43 Kivircik ewes and their 65 lambs, along with preweaning survival information of lambs. The prediction of lamb survival was performed on three datasets containing different features using decision tree, support vector machine (SVM), multilayer perceptron, logistic regression, random forest (RF), K-nearest neighbors, and boosting (B) ML algorithms. The accuracy, precision, recall, and F1 score values of the RF algorithm were 0.931, and the area under curve value was 0.966 for dataset 1, which included parity of dam, birth type and sex of lamb, and birth weight predictors, as well as postnatal lamb and ewe behaviors as features. In dataset 2, which includes principal component scores instead of lamb and ewe behaviors, the RF approach made classification with an accuracy of 0.909. In dataset 3, which includes the maternal behavior score as a feature, the RF and SVM algorithms showed similar performance (0.909 accuracy). These results indicate that, by using lamb and ewe behaviors in the postnatal 3-hour with ML methods, it is possible to classify lambs as either surviving or dying before weaning with high accuracy. In addition, it was determined that the ML algorithm that best adapted to the current study data was the RF classifier.</p></div>","PeriodicalId":17567,"journal":{"name":"Journal of Veterinary Behavior-clinical Applications and Research","volume":"74 ","pages":"Pages 37-45"},"PeriodicalIF":1.3000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Veterinary Behavior-clinical Applications and Research","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1558787824000522","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BEHAVIORAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
The aims of this study were to examine the relationship between lamb and ewe behaviors in postnatal 3-hour and lamb survival using machine learning (ML) algorithms and to determine the best ML classifier to predict lamb survival. The research data consisted of postnatal 3-hour behavior records of 43 Kivircik ewes and their 65 lambs, along with preweaning survival information of lambs. The prediction of lamb survival was performed on three datasets containing different features using decision tree, support vector machine (SVM), multilayer perceptron, logistic regression, random forest (RF), K-nearest neighbors, and boosting (B) ML algorithms. The accuracy, precision, recall, and F1 score values of the RF algorithm were 0.931, and the area under curve value was 0.966 for dataset 1, which included parity of dam, birth type and sex of lamb, and birth weight predictors, as well as postnatal lamb and ewe behaviors as features. In dataset 2, which includes principal component scores instead of lamb and ewe behaviors, the RF approach made classification with an accuracy of 0.909. In dataset 3, which includes the maternal behavior score as a feature, the RF and SVM algorithms showed similar performance (0.909 accuracy). These results indicate that, by using lamb and ewe behaviors in the postnatal 3-hour with ML methods, it is possible to classify lambs as either surviving or dying before weaning with high accuracy. In addition, it was determined that the ML algorithm that best adapted to the current study data was the RF classifier.
期刊介绍:
Journal of Veterinary Behavior: Clinical Applications and Research is an international journal that focuses on all aspects of veterinary behavioral medicine, with a particular emphasis on clinical applications and research. Articles cover such topics as basic research involving normal signaling or social behaviors, welfare and/or housing issues, molecular or quantitative genetics, and applied behavioral issues (eg, working dogs) that may have implications for clinical interest or assessment.
JVEB is the official journal of the Australian Veterinary Behaviour Interest Group, the British Veterinary Behaviour Association, Gesellschaft fr Tierverhaltensmedizin und Therapie, the International Working Dog Breeding Association, the Pet Professional Guild, the Association Veterinaire Suisse pour la Medecine Comportementale, and The American Veterinary Society of Animal Behavior.