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Our Foundational Research
Predicting Equine Health Outcomes Using Machine Learning Models Trained on Clinical Indicators and Limited Behavioral Data
Abstract
The capacity to perceive and anticipate the health status of horses is a critical aspect of equine veterinary care. Recent studies have shown that machine learning algorithms can accurately diagnose and classify animal diseases based on physiological signs. Using properties like heart rate, temperature, and other clinical parameters, the study offers a classification model developed from a publicly available dataset of more than 2,000 equine health records to predict health outcomes. Among the algorithms tested, including k-Nearest Neighbors (KNN), Decision Tree, and Light Gradient Boosting Machine (LightGBM), LightGBM achieved the highest validation accuracy at approximately 76%. Exploratory data analysis was conducted to visualize feature distributions and identify correlations, followed by preprocessing steps such as handling missing values and encoding categorical variables. The model was trained using five-fold cross-validation and fine-tuned for optimal performance. Among the factors contributing to the success of LightGBM were its ability to handle categorical features and its leaf-wise tree growth strategy, which improved learning efficiency on a moderately sized dataset. In addition to structured data, limited behavioral descriptors were incorporated using a language model to provide additional context regarding stress and discomfort. While these features had a smaller role, they created new opportunities for interpreting subtle health cues not included in clinical data alone. This study demonstrates the potential for predictive modeling to assist veterinarians in early diagnosis and treatment planning. Future work may focus on expanding the dataset and implementing more detailed behavioral and physiological data to improve model generalization.
Keywords: Equine health, machine learning, LightGBM, veterinary diagnostics, horse welfare, clinical data, outcome prediction.
Key Findings
LightGBM achieved the highest validation accuracy among all tested algorithms for equine health outcome prediction.
Comprehensive dataset analyzed using heart rate, temperature, and other clinical parameters.
Rigorous validation methodology ensuring model reliability and optimal performance tuning.
Clinical Significance
- Early Diagnosis Support: Predictive modeling assists veterinarians in early diagnosis and treatment planning
- Physiological Parameter Analysis: Heart rate, temperature, and clinical parameters serve as key predictors
- Behavioral Context Integration: Language model incorporation provides subtle health cues beyond clinical data
- Categorical Feature Handling: LightGBM's superior performance with mixed data types in veterinary applications
- Model Generalization: Foundation for expanding datasets with detailed behavioral and physiological data
Research Methodology
Our comprehensive research employed multiple machine learning algorithms to develop an accurate equine health classification model:
- Light Gradient Boosting Machine (LightGBM): Primary algorithm achieving 76% validation accuracy with efficient leaf-wise tree growth strategy
- K-Nearest Neighbors (KNN): Pattern recognition algorithm for comparative analysis of physiological data
- Decision Tree: Interpretable classification method for transparent health outcome prediction
- Exploratory Data Analysis: Feature distribution visualization and correlation identification
- Data Preprocessing: Missing value handling and categorical variable encoding
- Language Model Integration: Limited behavioral descriptors for stress and discomfort context
- Five-Fold Cross-Validation: Rigorous model validation and performance optimization
Other Key Research Areas
Exploring the latest developments in AI-powered equine health monitoring and assessment
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Put Research Into Practice
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