Liver Disease Detection and Classification Using Liver Data

Over the past decade, there has been a noticeable improvement in precision for disease. Classification through various machine learning methods. This advancement has been advantageous in revealing patterns and relationships in medical data, which has facilitated the creation of predictive models. The primary objective of this research paper is to present a concise review of the existing literature, comparing findings in the field of liver disease detection and prognosis. It centers on the utilization of liver function test data to identify the most influential factors for recognizing diseases. The primary aim is to effectively manage liver diseases. The study's outcomes demonstrate that the RBF, CMAC, AD Tree, and PSO-LS-SVM algorithms have considerably enhanced the accuracy of liver disease recognition and prognostication. Additionally, the paper explores prior research on liver function test data and its connection to predicting diabetes.