Comparison of machine learning algorithms and multiple linear regression for live weight estimation of Akkaraman lambs

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dc.authoridKOZAKLI, OZGE/0000-0003-4201-1157
dc.authoridceyhan, ayhan/0000-0003-2862-7369
dc.contributor.authorKozakli, Ozge
dc.contributor.authorCeyhan, Ayhan
dc.contributor.authorNoyan, Mevlut
dc.date.accessioned2024-11-07T13:25:04Z
dc.date.available2024-11-07T13:25:04Z
dc.date.issued2024
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractThis study was designed to predict the post-weaning weights of Akkaraman lambs reared on different farms using multiple linear regression and machine learning algorithms. The effect of factors the age of the dam, gender, type of lambing, enterprise, type of flock, birth weight, and weaning weight was analyzed. The data was collected from a total of 25,316 Akkaraman lambs raised at multiple farms in the & Ccedil;iftlik District of Ni & gbreve;de province. Comparative analysis was conducted by using multiple linear regression, Random Forest, Support Vector Machines (and Support Vector Regression), Extreme Gradient Boosting (XGBoost) (and Gradient Boosting), Bayesian Regularized Neural Network, Radial Basis Function Neural Network, Classification and Regression Trees, Exhaustive Chi-squared Automatic Interaction Detection (and Chi-squared Automatic Interaction Detection), and Multivariate Adaptive Regression Splines algorithms. In this study, the test dataset was divided into five layers using the K-fold cross-validation method. The performance of models was compared using performance criteria such as Adjusted R-squared (Adj-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}<^>{2}$$\end{document}), Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE) by utilizing test populations in the predicted models. Additionally, the presence of low standard deviations for these criteria indicates the absence of an overfitting problem. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}<^>{2}$$\end{document}The comparison results showed the Random Forest algorithm had the best predictive performance compared to other algorithms with Adj-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}<^>{2}$$\end{document}, RMSE, MAD, and MAPE values of 0.75, 3.683, 2.876, and 10.112, respectively. In conclusion, the results obtained through Multiple Linear Regression for the live weights of Akkaraman lambs were less accurate than the results obtained through artificial neural network analysis.
dc.description.sponsorshipNigde Omer Halisdemir University; Ministry of Agriculture and Forestry, General Directorate of Agricultural Research and Policies (TAGEM)
dc.description.sponsorshipThe authors would like to thank the Ministry of Agriculture and Forestry, General Directorate of Agricultural Research and Policies (TAGEM) and sheep farmers for their support for this study.
dc.identifier.doi10.1007/s11250-024-04049-0
dc.identifier.issn0049-4747
dc.identifier.issn1573-7438
dc.identifier.issue7
dc.identifier.pmid39225879
dc.identifier.scopus2-s2.0-85202956077
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s11250-024-04049-0
dc.identifier.urihttps://hdl.handle.net/11480/14485
dc.identifier.volume56
dc.identifier.wosWOS:001305486800003
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofTropical Animal Health and Production
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectArtificial neural network analysis
dc.subjectK-Fold cross validation
dc.subjectLive weight
dc.subjectRandom Forest
dc.titleComparison of machine learning algorithms and multiple linear regression for live weight estimation of Akkaraman lambs
dc.typeArticle

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