HEPATITIS DISEASES PREDICTION USING MACHINE-LEARNING TECHNIQUES

Abstract

The importance of research that contributes to the early diagnosis and management of lethal diseases is critical to society, and hepatitis is one of these killer diseases. Hepatitis is a life-threatening condition that develops when the liver becomes enlarged and injured. As a result, the primary goal of this article is to analyze the hepatitis dataset in order to accurately forecast outcome accuracy and dependability. Six machine learning classification methods: Support Vector Machines, Gaussian Naive Bayes, Logistic Regression, Decision Tree, K Nearest Neighbors, and Multiplayer Perceptron were tested on hepatitis dataset and a confusion matrix was plotted for each of the classification models. The accuracy, precision, and recall criteria were used to make the comparison. For each model, the accuracy was assessed using the root mean square value and mean absolute error. The selected algorithms, particularly the Multiplayer Perceptron (87%) and Logistic Regression (87%) algorithms, showed high accuracy rates. Furthermore, with a minimal root mean error of 0.35 and a minimal mean absolute error of 0.12 and 0.13, the two algorithms are the most dependable of all the methods