Expert System for Detecting Cat Skin Disease using Certainty Factor Method

Yessi Jusman, Muhammad Ahdan Fawwaz Nurkholid, Amin Musthofa, Wita Yulianti, Farah Ramadhani


With the increasing number of people contaminated with cat disease, many veterinarians specializing in cats in Indonesia open up a practice in big cities only. Thus, it is not uncommon for cat owners who are late to provide treatment for skin diseases since the initial symptoms occur. Based on the problems to overcome skin diseases in cats, it is necessary to build a computerized system that has knowledge such as veterinarians and the system can be a tool in diagnosing types of diseases and provide solutions for treatment and prevention with the expert system of Certainty Factor (CF), because the Certainty Factor (CF) method is to prove whether a fact is certain or uncertain in the form of a metric that is usually used in expert systems. The results of the system are to help users, among others, veterinarians in diagnosing skin diseases in cats and animal owners, especially cats to find skin diseases in cats. It is expected to make it easier for doctors and cat owners to determine the type of skin disease based on existing symptoms and get the right treatment method. 


Animals; Cats; Diseases; Skin; Certainty Factor; Expert System

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Corti, M., et al., Rare human skin infection with Corynebacterium ulcerans: transmission by a domestic cat. Infection, 2012. 40(5): p. 575-578.

Amarathunga, A., et al., Expert system for diagnosis of skin diseases. International Journal of Scientific & Technology Research, 2015. 4(01): p. 174-178.

Choudhury, D., A. Naug, and S. Ghosh. Texture and color feature based WLS framework aided skin cancer classification using MSVM and ELM. in 2015 Annual IEEE India Conference (INDICON). 2015.

Yadav, N., N. Yadav, and V.K. Narang, Skin diseases detection models using image processing: A survey. International Journal of Computer Applications, 2016. 137(12): p. 0034-0039.

Ansari, U.B. and T. Sarode, Skin cancer detection using image processing. Int Res J Eng Technol, 2017. 4(4): p. 2875-2881.

Kadhim, Q.K., Classification of human skin diseases using data mining. International Journal of Advanced Engineering Research and Science, 2017. 4(1).

Gupta, A., et al. Adaptive thresholding for skin lesion segmentation using statistical parameters. in 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA). 2017. IEEE.

Agarwal, A., et al. Automated computer vision method for lesion segmentation from digital dermoscopic images. in 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON). 2017.

Pandey, P., et al., A Multi-scale Retinex with Color Restoration (MSR-CR) Technique for Skin Cancer Detection, in Soft Computing for Problem Solving. 2019, Springer. p. 465-473.

Raza, M.A.A., M.S. Liaqat, and M. Shoaib. A Fuzzy Expert System Design for Diagnosis of Skin Diseases. in 2019 2nd International Conference on Advancements in Computational Sciences (ICACS). 2019. IEEE.

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