A Comparative Analysis for Prediction of Liver Cirrhosis Using Deep Learning Methods

Liver cirrhosis is a prevalent and potentially life-threatening condition characterized by the irreversible scarring of the liver tissue. In this study, we propose a comparative approach employing three deep learning models, Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP) Classifier, and Logistic Regression (LR) to enhance the diagnostic accuracy of liver cirrhosis. The primary objective of this paper was to develop and evaluate an automated prediction system. This system utilizes a comprehensive database of cirrhosis data, with a particular focus on improving the detection of liver cirrhosis. The goal of this study is to examine how well three distinct supervised deep learning models performed when it comes to liver cirrhosis detection utilizing actual inter-patient records. Four key measures were used in the study to assess the models’ performances: F1-score, accuracy, precision, and recall. Finally, comparative analysis has been made to showcase the performances indices of this three deep learning models.