Horse Surgery and Survival Prediction with Artificial Intelligence Models: Performance Comparison of Original, Imputed, Balanced, and Feature-Selected Datasets

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dc.contributor.authorCihan, Pinar
dc.date.accessioned2024-10-29T17:59:54Z
dc.date.available2024-10-29T17:59:54Z
dc.date.issued2024
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractArtificial intelligence (AI) technology, while less advanced than in human medicine, holds significant potential in the field of veterinary medicine. This technology offers a range of essential benefits, such as disease diagnosis, treatment planning, disease control, and overall animal health improvement. Based on clinical data, this study uses 15 AI models to predict the necessity of surgery and the likelihood of survival in horses displaying symptoms of acute abdominal pain (colic). By comparing surgical and survival predictions across the original, imputed missing values, and balanced datasets, we determine the most effective dataset based on the average accuracy of the 15 AI models. Furthermore, we explore the potential for improved accuracy with a reduced feature set by calculating feature importance scores for surgery and survival predictions. Our results indicate that the balanced dataset achieved the highest average accuracy for predicting surgery and survival, with 80.76% and 77.96%, respectively. The Random Forest (RF) model outperformed others as the most accurate model for both surgery (accuracy = 85.83, Area Under the Curve [AUC] = 0.906) and survival prediction (accuracy = 80.75, AUC = 0.888). It was observed that reducing the number of features in the dataset by 56% led to an increase in surgery prediction accuracy to 86.38%. Similarly, when the number of features was reduced by 24% for survival prediction, the prediction performance increased to 83.75%. This study emphasizes the importance of the precise implementation of artificial intelligence techniques in veterinary medicine, which can significantly enhance model performance.
dc.identifier.doi10.9775/kvfd.2023.30908
dc.identifier.issn1300-6045
dc.identifier.issn1309-2251
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85185936155
dc.identifier.scopusqualityQ3
dc.identifier.trdizinid#BAŞV!
dc.identifier.urihttps://doi.org/10.9775/kvfd.2023.30908
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1264554
dc.identifier.urihttps://hdl.handle.net/20.500.11776/14870
dc.identifier.volume30
dc.identifier.wosWOS:001147142700001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherKafkas Univ, Veteriner Fakultesi Dergisi
dc.relation.ispartofKafkas Universitesi Veteriner Fakultesi Dergisi
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial intelligence
dc.subjectData balancing
dc.subjectFeature selection
dc.subjectHorse colic
dc.subjectPrediction
dc.subjectSMOTE
dc.titleHorse Surgery and Survival Prediction with Artificial Intelligence Models: Performance Comparison of Original, Imputed, Balanced, and Feature-Selected Datasets
dc.title.alternativeHorse Surgery and Survival Prediction with Artificial Intelligence Models: Performance Comparison of Original, Imputed, Balanced, and Feature- Selected Datasets
dc.typeArticle

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