A Machine Learning Approach for Diagnosis of Hepatitis

Unbeknownst to themselves, hundreds of millions of individuals worldwide are dealing with a chronic infection. Without exaggeration, chronic hepatitis is one of the most significant medical and social issues in any nation in the world. The authors created an accurate machine-learning (ML) model to increase forecast success rates. The medical information of 155 patients is contained in the publicly accessible hepatitis dataset, which can be found on the UCI machine learning repository. The authors have applied ML classifiers such as Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Logistic Regression (LR), and Gaussian Naive Bayes (GNB) to forecast the disease. RF had the highest accuracy, at 100%, of all the ML classification approaches that were used.