Machine Learning-Based Classification Algorithms for Predicting Hepatitis C: A Comprehensive Analysis

Abstract

A disease's accurate diagnosis is one of the most important tasks in the medical field. The most dangerous sickness, which continuously affects a many individuals is hepatitis disease, hence there is a need to automate the disease diagnosis. This study evaluates the diagnostic performance in terms of various parameters and optimization techniques using a range of machine-learning algorithms on a hepatitis dataset. A large dataset that included clinical history, lab test results, and demographic data was used. To get the data ready for analysis, preprocessing techniques such as data cleaning, data discretization, and data normalization were used. The algorithms included in this study are Multi-Layer-Perceptron, Support Vector Machine, Naive Bayes and Random Forest, these algorithms were trained and assessed using metrics including accuracy, recall, precision and F1 score. To minimize overfitting, the model's performance was checked using K-fold cross-validation. ReLU activation function was applied to Multi-Layer-Perceptron for solving the vanishing gradient problem. The classification accuracy scores demonstrate promising outcomes, with SVM scoring 91.86%, NB scoring 89.43%, RF scoring 89.43% and MLP scoring 92.68%. Among all algorithms MLP shows highest frequency.