Investigating of Machine Learning Based Algorithms for Liver Cirrhosis Prediction
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 meth...
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| Format: | Article |
| Language: | English |
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Bilijipub publisher
2024-03-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| Online Access: | https://aeis.bilijipub.com/article_193341_9cd6661d2981d10bdd36219bbdc13178.pdf |
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| _version_ | 1823856465467670528 |
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| author | Young Choi Eunsung Oh |
| author_facet | Young Choi Eunsung Oh |
| author_sort | Young Choi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e1528425dfcb40e1a98a5c2e89cd7d4f |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-e1528425dfcb40e1a98a5c2e89cd7d4f2025-02-12T08:47:47ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632024-03-010030111513010.22034/aeis.2024.446087.1177193341Investigating of Machine Learning Based Algorithms for Liver Cirrhosis PredictionYoung Choi0Eunsung Oh1Graduate School of Logistics, Incheon National University, Incheon 22012, South KoreaHANSEO University, Seosan-Si, Chungcheongnam-do, 31962, South KoreaThis 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.https://aeis.bilijipub.com/article_193341_9cd6661d2981d10bdd36219bbdc13178.pdfliver cirrhosis predictionmachine learninghealthcare managementdisease prognosisperformance analysis |
| spellingShingle | Young Choi Eunsung Oh Investigating of Machine Learning Based Algorithms for Liver Cirrhosis Prediction Advances in Engineering and Intelligence Systems liver cirrhosis prediction machine learning healthcare management disease prognosis performance analysis |
| title | Investigating of Machine Learning Based Algorithms for Liver Cirrhosis Prediction |
| title_full | Investigating of Machine Learning Based Algorithms for Liver Cirrhosis Prediction |
| title_fullStr | Investigating of Machine Learning Based Algorithms for Liver Cirrhosis Prediction |
| title_full_unstemmed | Investigating of Machine Learning Based Algorithms for Liver Cirrhosis Prediction |
| title_short | Investigating of Machine Learning Based Algorithms for Liver Cirrhosis Prediction |
| title_sort | investigating of machine learning based algorithms for liver cirrhosis prediction |
| topic | liver cirrhosis prediction machine learning healthcare management disease prognosis performance analysis |
| url | https://aeis.bilijipub.com/article_193341_9cd6661d2981d10bdd36219bbdc13178.pdf |
| work_keys_str_mv | AT youngchoi investigatingofmachinelearningbasedalgorithmsforlivercirrhosisprediction AT eunsungoh investigatingofmachinelearningbasedalgorithmsforlivercirrhosisprediction |