Abstract: This research paper presents a comprehensive study on the application of Support Vector Machine (SVM) for Hepatitis disease diagnosis and prediction. The proposed SVM-driven model achieves a remarkable accuracy of 96% in predicting Hepatitis with a minimum mean square error. Additionally, the potential integration of Convolutional Neural Networks (CNN) for anticipating the occurrence of various diseases is discussed, pointing towards future research directions in this area

Keywords: Hepatitis, disease diagnosis, prediction, Support Vector Machine (SVM), Convolutional Neural Networks (CNN)

| DOI: 10.17148/IARJSET.2023.107100