Authors - Kanyanut Homsapaya, Bonnyakorn Leelakarnsakul, Waraporn Aumarm, Orawan Watchanupaporn Abstract - Congestive heart failure (CHF) in cats is a serious condition characterized by the heart’s inability to pump blood effectively, resulting in fluid accumulation in the lungs (pulmonary edema), chest cavity (pleural effusion), or abdomen (ascites). This life-threatening disorder manifests with clinical signs such as respiratory distress, profound lethargy with progression often culminating in organ failure if left untreated. Early detection is critical, and the integration of artificial intelligence (AI) offers significant potential for improving outcomes by analyzing diagnostic imaging, physiological data, and medical records to predict CHF onset and facilitate timely, targeted interventions. In this experiment, the data consists of 181 cats diagnosed with pulmonary edema, collected from Kasetsart University Veterinary Medicine Hospital. It includes relevant features such as clinical signs, diagnostic test results, medical history, and physiological data, providing a comprehensive basis for building a predictive model for congestive heart failure. The research utilizes multiple algorithms, including Support Vector Machine (SVM), KNN etc. to develop predictive models for congestive heart failure. The performance of each model is evaluated, and the algorithm with the highest accuracy and reliability is selected as the optimal approach. This work demonstrates the superior performance of Support Vector Machine (SVM) in predicting congestive heart failure (CHF), underscoring their significant advantages in both accuracy and efficiency compared to other models.
