Multiclass liver disease prediction with adaptive data preprocessing and ensemble modeling

Abstract

Liver disease includes a range of health conditions resulting from various factors that impact the normal functioning of the liver over an extended period. Effective treatment of Hepatitis C, a type of liver disease, can be significantly enhanced by accurately and timely predicting risk factors and severity, covering different stages like fibrosis and cirrhosis. This research utilizes the Hepatitis C dataset from the UCI repository to present a comprehensive framework for the prediction of liver disease across various stages. We have proposed an adaptive data preprocessing technique designed to enhance the efficacy of our foundational ML models. This method incorporates class specific mean imputation, outlier rejection, log normalization, feature selection, feature scaling and data balancing. The proposed preprocessing technique underwent rigorous analysis. Furthermore, we also proposed a set of ensemble models by combining basic ML classifiers to further improve the accuracy of liver disease predictions. The best model underwent rigorous parameter optimization and exhibited remarkable training and testing accuracy, reaching up to 99.87% and 99.80% respectively, outperforming earlier research on the dataset. A user-friendly interface was developed to enhance user interaction and practical use, enabling medical professionals to effortlessly enter patient information and promptly receive assessments regarding the risk factors associated with liver disorders.