From Prediction to Precision: Explainable AI-Driven Insights for Targeted Treatment in Equine Colic
Colic is a leading cause of mortality in horses, demanding precise and timely interventions. This study integrates machine learning and explainable artificial intelligence (XAI) to predict survival outcomes in horses with colic, using clinical, procedural, and diagnostic data. Random forest and XGBo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/15/2/126 |
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| author | Bekir Cetintav Ahmet Yalcin |
| author_facet | Bekir Cetintav Ahmet Yalcin |
| author_sort | Bekir Cetintav |
| collection | DOAJ |
| description | Colic is a leading cause of mortality in horses, demanding precise and timely interventions. This study integrates machine learning and explainable artificial intelligence (XAI) to predict survival outcomes in horses with colic, using clinical, procedural, and diagnostic data. Random forest and XGBoost emerged as top-performing models, achieving F1 scores of 85.9% and 86.1%, respectively. SHAP (Shapley additive explanations) was employed to provide interpretable insights, offering both global and local explanations for model predictions. The analysis revealed that key features, such as pulse rate, lesion type, and total protein levels, significantly influenced survival likelihood. Local interpretations highlighted the unique contribution of clinical factors to individual cases, enabling personalized insights that guide targeted treatment strategies. These tailored predictions empower veterinarians to prioritize interventions based on the specific conditions of each horse, moving beyond generalized care protocols. By combining predictive accuracy with interpretability, this study advances precision veterinary medicine, enhancing outcomes for equine colic cases and setting a benchmark for future applications of AI in animal health. |
| format | Article |
| id | doaj-art-3af89fe1861547388b17f99f40957136 |
| institution | Kabale University |
| issn | 2076-2615 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj-art-3af89fe1861547388b17f99f409571362025-01-24T13:17:40ZengMDPI AGAnimals2076-26152025-01-0115212610.3390/ani15020126From Prediction to Precision: Explainable AI-Driven Insights for Targeted Treatment in Equine ColicBekir Cetintav0Ahmet Yalcin1Department of Biostatistics, Veterinary Faculty, Burdur Mehmet Akif Ersoy University, 15030 Burdur Merkez, TurkeyInstitute of Science, Burdur Mehmet Akif Ersoy University, 15030 Burdur Merkez, TurkeyColic is a leading cause of mortality in horses, demanding precise and timely interventions. This study integrates machine learning and explainable artificial intelligence (XAI) to predict survival outcomes in horses with colic, using clinical, procedural, and diagnostic data. Random forest and XGBoost emerged as top-performing models, achieving F1 scores of 85.9% and 86.1%, respectively. SHAP (Shapley additive explanations) was employed to provide interpretable insights, offering both global and local explanations for model predictions. The analysis revealed that key features, such as pulse rate, lesion type, and total protein levels, significantly influenced survival likelihood. Local interpretations highlighted the unique contribution of clinical factors to individual cases, enabling personalized insights that guide targeted treatment strategies. These tailored predictions empower veterinarians to prioritize interventions based on the specific conditions of each horse, moving beyond generalized care protocols. By combining predictive accuracy with interpretability, this study advances precision veterinary medicine, enhancing outcomes for equine colic cases and setting a benchmark for future applications of AI in animal health.https://www.mdpi.com/2076-2615/15/2/126equine colicexplainable artificial intelligence (XAI)machine learning in veterinary medicineSHAPprecision animal health managementtargeted veterinary medicine |
| spellingShingle | Bekir Cetintav Ahmet Yalcin From Prediction to Precision: Explainable AI-Driven Insights for Targeted Treatment in Equine Colic Animals equine colic explainable artificial intelligence (XAI) machine learning in veterinary medicine SHAP precision animal health management targeted veterinary medicine |
| title | From Prediction to Precision: Explainable AI-Driven Insights for Targeted Treatment in Equine Colic |
| title_full | From Prediction to Precision: Explainable AI-Driven Insights for Targeted Treatment in Equine Colic |
| title_fullStr | From Prediction to Precision: Explainable AI-Driven Insights for Targeted Treatment in Equine Colic |
| title_full_unstemmed | From Prediction to Precision: Explainable AI-Driven Insights for Targeted Treatment in Equine Colic |
| title_short | From Prediction to Precision: Explainable AI-Driven Insights for Targeted Treatment in Equine Colic |
| title_sort | from prediction to precision explainable ai driven insights for targeted treatment in equine colic |
| topic | equine colic explainable artificial intelligence (XAI) machine learning in veterinary medicine SHAP precision animal health management targeted veterinary medicine |
| url | https://www.mdpi.com/2076-2615/15/2/126 |
| work_keys_str_mv | AT bekircetintav frompredictiontoprecisionexplainableaidriveninsightsfortargetedtreatmentinequinecolic AT ahmetyalcin frompredictiontoprecisionexplainableaidriveninsightsfortargetedtreatmentinequinecolic |