Prediction Liver Diseases based on Machine Learning and Deep Learning Techniques: A Review

Abstract

Various critical issues in liver diseases include cirrhosis, hepatitis, and liver cancer, which can be fatal. They indeed require early diagnosis with appropriate diagnosis of the disease. The different conventional diagnostic methods generally can't identify these diseases during their early stages; consequently, prognosis is not always good. Recently, in subsequence to improve this gap, ML/DL has emerged as the tool for transformation. It gives an overview of different ML and DL models used for predicting liver diseases, including supervised, unsupervised, semi-supervised learning, and reinforcement learning, and emphasizes the better performance that deep learning models like Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks are providing in handling complex medical data. These DL models perform significantly better in diagnostic accuracy when compared to the traditional ML methods, hence holding tremendous potential in their medical applications. Besides, hybrid and ensemble methods, which are combined models, are emphasized for their ability to overcome the limitations of individual algorithms and enhance diagnostic precision and robustness. This study further underlines the need to develop more advanced DL methodologies for the early detection and intervention in liver diseases, which is necessary to reduce the global burden and improve patient outcomes.