International Conference on Emerging Trends and Applications in Artificial Intelligence , İstanbul, Turkey, 8 - 09 October 2023
Cats are close friends who live with us in all aspects of life. Many
diseases endanger the quality of life of cats that live with us. One of the most
dangerous is infectious peritonitis in cats, also known as FIP; which is a
coronavirus that affects a cat's overall metabolism. There is no specific treatment
for FIP and existing drugs are difficult to find and very expensive; therefore, early
detection is very important. The most important thing for early detection is to
know the body changes caused by the disease, i.e., symptoms, to take appropriate
measures. By collecting and interpreting information such as the combination of
symptoms, the age at which cats are most common, and the breeds most
encountered, cat owners can take precautions even when they cannot be alert.
Therefore, in this study, an early detection method for FIP disease in cats is
introduced by making predictions using Naive Bayes algorithm. The dataset
includes of 300 FIP symptoms used by Jones et al. , and from Ümraniye Vita
Veterinary Clinic data were obtained from 150 cats who did not have FIP but
went to the clinic for other diseases. This generated dataset is resampled using
the Smote algorithm to enlarge the dataset. Then the Google Colab program is
used to create a naive Bayesian model using the Python programming language.
For this study a model is built using the Naive Bayes algorithm, and it is shown
that the model can predict the FIP disease with 96% accuracy.