A Web Application for Hepatitis C Prediction Using Machine Learning with Explainable AI

Abstract

Hepatitis C continues to be a major health concern in the Philippines, yet machine learning based decision support tools with Explainable AI remain limited in number. This study aims to develop an interpretable, web-based binary classification model for HCV prediction using biochemical markers from a publicly available dataset from UCI. Data preprocessing involved removal of insignificant attributes, encoding, normalization, and handling of missing values using KNN imputation. Class imbalance was addressed using SMOTE, and five supervised machine learning algorithms—K-Nearest Neighbors, Random Forest, Logistic Regression, Support Vector Machine, and Extreme Gradient Boosting—were evaluated using GridSearchCV with 10-fold cross validation. Among all model configurations, the Random Forest model trained with SMOTE, no imputation, and no hyperparamter tuning achieved perfect performance (100% recall, accuracy, precision, and F1 score), and was implemented in a functional web application. Explainability was provided through SHAP and LIME. SHAP revealed AST, ALT, and BIL as the most influential features, aligning with domain knowledge on liver enzyme activity in HCV patients. LIME explanations further supported model transparency at the individual prediction level. This study not only demonstrates the viability of interpretable machine learning for HCV prediction but also contributes a usable web application that may aid in early disease detection and patient education.