Liver Disease Classification Using Machine Learning
Hepatitis C virus (HCV) is a significant cause of liver-related diseases including acute and chronic hepatitis, cirrhosis, and hepatocellular carcinoma. Despite the availability of advanced treatments, underdiagnosis remains a critical challenge, particularly in resource-limited settings. This study explores the application of machine learning algorithms, specifically the K-Nearest Neighbors (KNN) method, to enhance the diagnosis of HCV by classifying patients into healthy, potentially diseased, and diseased categories based on liver function test results. Using a biomedical dataset of 615 patients, the model achieved high accuracy (99%), precision (98%), and sensitivity (99%), indicating its potential effectiveness in identifying HCV-infected individuals. The study highlights the importance of feature selection in improving model performance and discusses the implications of the findings for enhancing HCV diagnosis and management