A decision support system for detecting FIP disease in cats based on machine learning methods

Abstract

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. [11], 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.