Background: Automated classification systems using Artificial Intelligence (AI) and Machine Learning (ML) can enhance accuracy and efficiency in diagnosing pet skin diseases within veterinary medicine. Objectives: This study created a system that classifies pet skin diseases by evaluating multiple ML models to determine which method is most effective. Design: Comparative experimental study. Methods: Pet skin disease images were obtained from AIHub. Models, including Multi-Layer Perceptron (MLP), Boosted Stacking Ensemble (BSE), H2O AutoML, Random Forest, and Tree-based Pipeline Optimization Tool (TPOT), were trained and their accuracy assessed. Results: The TPOT achieved the highest accuracy (94.50 percent), due to automated pipeline optimization and ensemble learning. H2O AutoML also performed well at 94.25 percent, illustrating the effectiveness of automated selection for intricate imaging tasks. Other models scored lower. Conclusion: These findings highlight the potential of AI-driven solutions for faster and more precise pet skin disease diagnoses. Future investigations should incorporate broader disease varieties, multimodal data, and clinical validations to solidify the practicality of these approaches in veterinary medicine.

INTRODUCTION
    Related Works
    Proposed Method
RESULTS
    Data Collection and Preprocessing
    Model Performance
    Confusion Matrix and ROC Analysis
DISCUSSION
CONCLUSION
ACKNOWLEGEMENT
REFERENCES

• Bokyung Kim(Department of Physical Therapy, Changshin University, Changwon, Republic of Korea)
• Yusung Jang(Department of Physical Therapy, Gangdong University, Eumseong, Republic of Korea)
• Wansuk Choi(Department of Physical Therapy, Kyungwoon University, Gumi, Republic of Korea) Corresponding author