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Results indicate high accuracy relative to traditional approaches, supporting improved diagnosis and management, potentially lowering healthcare costs. The work also suggests broader socio-economic benefits from applying ML in clinical risk prediction.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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Systems\u003Cbr>e-ISSN:2821-0263 Volume 03, Issue 01, 2024\u003Cbr>Journal Web Page: [https://aeis.bilijipub.com](https://aeis.bilijipub.com) | |\n| --- | --- | --- |\n\nInvestigating of Machine Learning Based Algorithms for Liver Cirrhosis Prediction  \nYoung-Seo Choi 1 * , Eunsung Oh 2   \n1 Graduate School of Logistics, Incheon National University, Incheon, 22012, South Korea  \n2 HANSEO University, Seosan-Si, Chungcheongnam-do, 31962, South Korea  \nHighlights  \n\n| ➢ Utilizing machine learning algorithms for accurate prediction of liver cirrhosis.\u003Cbr>➢ Comprehensive experimentation and rigorous evaluation to enhance accuracy rates.\u003Cbr>➢ Novelty lies in substantial improvements in prediction accuracy rates compared to existing methods.\u003Cbr>➢ Potential to revolutionize liver cirrhosis diagnosis and management practices for improved patient outcomes. |  |\n| --- | --- |\n| Article Info | Abstract\u003Cbr>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 compared 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. |\n| Received: 11 February 2024\u003Cbr>Received in revised: 10 March 2024\u003Cbr>Accepted: 30 March 2024\u003Cbr>Available online: 30 March 2024\u003Cbr>Keywords |  |\n| Liver Cirrhosis Prediction, Machine Learning, Healthcare Management, Disease Prognosis, Performance Analysis. |  |\n| ©2024 by the authors.\u003Cbr>Published by the Bilijipub publisher (Zenith Sustainable Energy Institute) .\u003Cbr>This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) . |  |\n\n1. Introduction  \nAcquiring The prevalence of liver cirrhosis remains a significant concern in healthcare due to its escalating incidence worldwide[1] . Early detection and timely intervention are pivotal in mitigating its progression and associated complications[2,3] . The ability to predict and identify liver cirrhosis at its incipient stages holds paramount importance in enhancing patient outcomes and  \n* Corresponding Author: Young-Seo Choi [Email:](Email: yschoi@inu.re.kr)[ ](Email: yschoi@inu.re.kr)[yschoi@inu.re.kr](Email: yschoi@inu.re.kr)  \n[https://doi.org/10.22034/aeis.2024.446087.1177](https://doi.org/10.22034/aeis.2024.446087.1177)  \nreducing morbidity and mortality rates associated with this condition.  \nMachine learning (ML) techniques have emerged as indispensable tools in medical data analysis and mining, risk assessment and revolutionizing the approach to disease prediction[4,5] . Leveraging vast datasets, ML algorithms offer unprecedented capabilities in uncovering patterns and relationships within medical data[6–8] . In  \nliver cirrhosis, the application of these techniques enables more precise forecasting and early detection, thereby aiding clinicians in making informed decisions for patient care.  \nDespite notable strides in liver cirrhosis prediction using machine learning, ","cbCaivxa47sxuTS1","https://ap.wps.com/l/cbCaivxa47sxuTS1","pdf",791112,16,"English","# Introduction\n## Background and motivation\n## Research problem and objectives\n# Abstract\n## Study purpose and approach\n## Evaluation and novelty\n# Keywords","[{\"question\":\"What is the main goal of the study on liver cirrhosis prediction?\",\"answer\":\"The study focuses on using machine learning algorithms to predict liver cirrhosis with improved accuracy and reliability, addressing limitations of current methods.\"},{\"question\":\"How does the paper support its claims about improved performance?\",\"answer\":\"It reports comprehensive experimentation and rigorous evaluation on liver cirrhosis datasets, including comparisons showing significant accuracy improvements over existing approaches.\"},{\"question\":\"Why is early liver cirrhosis prediction considered important?\",\"answer\":\"Early detection and timely intervention are emphasized as pivotal for mitigating progression and complications, improving patient outcomes, and potentially reducing healthcare costs.\"}]","Investigating of Machine Learning Based Algorithms for Liver Cirrhosis Prediction | PDF",1789641785]