Horse surgery and survival prediction with artificial intelligence models: performance comparison of original, imputed, balanced, and feature- selected datasets

Pınar  CİHAN

Horse surgery and survival prediction with artificial intelligence models: performance comparison of original, imputed, balanced, and feature- selected datasets

2024

Pınar CİHAN

Artificial 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.

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