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Diagnosis of Pet by Using FCM Clustering

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(2), pp.39-44
  • DOI : 10.9708/jksci.2021.26.02.039
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : January 25, 2021
  • Accepted : February 23, 2021
  • Published : February 26, 2021

Kwang Baek Kim ORD ID 1

1신라대학교

Accredited

ABSTRACT

In this paper, we propose a method of disease diagnosis system that can diagnose the health status of household pets for the people who lack veterinary knowledge. The proposed diagnosis system holds 50 different kinds of diseases with the symptoms for each of them as a database to provide results from symptom input. Each disease database has its own symptom codes for a disease, and by using the disease database, FCM clustering technique is applied to disease which outputs membership degree to determine diseases close to the input symptom as a pet diagnosis result. The implementation results of the proposed pet diagnosis system were obtained by the number of selected symptoms and the possibility values of the diseases that have the selected symptoms being sorted in descending order to derive top 3 diseases closest to the pet’s symptom.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.