Leveraging Expert Knowledge for Mobile Livestock Care: Combining AHP and Naïve Bayes for Diagnosis, Treatment, and Management

Abstract

This study successfully designed and developed a smartphone application for livestock disease diagnosis, treatment, and reporting. The agile development framework Extreme Programming (XP) ensured efficient iteration and adaptation based on user feedback. Additionally, the integration of the Analytical Hierarchy Process (AHP) with veterinary expert input facilitated the prioritization of disease possibilities within the app. Furthermore, the application of Naive Bayes probability allowed the system to rank diseases based on them likelihood, enhancing the accuracy of diagnoses. Workshops, field observations, group discussions, and interviews with senior veterinary experts were used as data collecting and final product assessment tools to ensure it met the original criteria. Purposive sampling was used to distribute the application to 90 smartphone users who work in veterinary clinics, including 49 senior veterinary medicine students. The proposed system offers benefits such as improved healthcare access, early disease detection, enhanced disease management, and strengthened livestock health surveillance. This multifaceted approach holds significant promise for improving livestock health management, particularly in resource-limited settings.