Mobile-based Cattle Infectious Disease Prediction System

It is not easy to notice the infectious disease that can spread among the herd of animals, such as cattle, sheep, etc without daily care and diagnosis. However, it is expensive to give daily care in detail by human to them due to large number of animals and expensive costs of veterinary. This paper presents a novel prediction algorithm for diagnosing cattles infectious disease with mobile based information system. It mainly uses Nave Bayes classifiers to classify the level of risk affected to cattle by observing six baseline syndrome patterns of animal health such as body weight, ambulatory lameness, decreased feed intake/milk production, respiratory, skin/ocular/mammary and gastrointestinal signs. The system firstly uploads the abnormal information detected from cattle to our mobile based system. It then analyzes them using Nave Bayes classification algorithm to know what kind of diseases currently affect to them and give suggestion the method of cure for earlier protection to them. The paper then performs the experiments with the cattle baseline patterns surveyed from different five livestock area in Myanmar. The results show that our prediction algorithm produces accurate results (95.6 % for almost each 600 data patterns obtained, from each area, about 3000 in total) and could help those who are thriving in need of human veterinary to analyze their cattles health conditions.