Investigating of Machine Learning Based Algorithms for Liver Cirrhosis Prediction

Document Type : Original Article


1 Graduate School of Logistics, Incheon National University, Incheon 22012, South Korea

2 Hanseo University, Chungcheongnam-do 31962, South Korea


This paper presents a study on utilizing machine learning algorithms for predicting liver cirrhosis with a focus on enhancing accuracy rates. Through comprehensive experimentation and rigorous evaluation using liver cirrhosis datasets, the research demonstrates the effectiveness of the proposed methodology in addressing the research gap and yielding notably accurate predictions. The novelty lies in the extensive experimentation and performance evaluations conducted, which reveal substantial improvements in prediction accuracy rates compared to existing methods. Specific numerical results show significant enhancements, with the proposed algorithm achieving high accuracy rate compare to traditional approaches. These findings not only underscore the superiority of the algorithm but also highlight its potential to revolutionize liver cirrhosis diagnosis and management practices, potentially leading to improved patient outcomes and reduced healthcare costs. Beyond medicine, the integration of machine learning algorithms in liver cirrhosis prediction could have broader socio-economic implications, including enhanced resource allocation and healthcare delivery optimization.