Horse-Expert: An aided expert system for diagnosing horse diseases

Open access

Abstract

In contrast to the rapid development of the horse husbandry in China, the ability of horse veterinarians to diagnose diseases has not been improved and only a few domain experts have considerable expertise. At present, many expert systems have been developed for diseases diagnosis, but few for horse diseases diagnosis have been studied in depth. This paper presents the design and development of a computer-aided expert system for diagnosing horse diseases. We suggest an approach for diagnosis of horse diseases based on the analysis of diagnostic characteristics and the experiential knowledge of domain experts. It is based on using evidence-weighted uncertainty reasoning theory, which is a combination of evidence theory and an uncertainty pass algorithm of confidence factors. It enables drawing of inferences with atypical clinical signs and the uncertainty of the user’s subjective understanding. It reduces the influence of subjective factors on diagnostic accuracy. The system utilizes a user friendly interface for users and requests a confidence factor from users when feedback is given to the system. Horse-Expert combines the confidence factors with weight factors assigned to clinical signs by experts during the knowledge acquisition process to make diagnostic conclusions. The system can diagnose 91 common horse diseases, and provides suggestions for appropriate treatment options. In addition, users can check the medical record through statistical charts. The system has been tested in seven demonstration areas of Xinjiang province in northwestern China. By constantly maintaining and updating the knowledge base, the system has potential application in veterinary practice.

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Polish Journal of Veterinary Sciences

The Journal of Committee of Veterinary Sciences of Polish Academy of Sciences and University of Warmia and Mazury in Olsztyn

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IMPACT FACTOR 2016: 0.697
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CiteScore 2016: 0.73

SCImago Journal Rank (SJR) 2016: 0.315
Source Normalized Impact per Paper (SNIP) 2016: 0.486

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