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Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy

BACKGROUND: Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning. The global community has recently witnessed an increase in the mortality rate due to liver disease. This could be attributed to many factors, among which are human habits, awar...

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Autores principales: Dalal, Surjeet, Onyema, Edeh Michael, Malik, Amit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Baishideng Publishing Group Inc 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782838/
https://www.ncbi.nlm.nih.gov/pubmed/36569269
http://dx.doi.org/10.3748/wjg.v28.i46.6551
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author Dalal, Surjeet
Onyema, Edeh Michael
Malik, Amit
author_facet Dalal, Surjeet
Onyema, Edeh Michael
Malik, Amit
author_sort Dalal, Surjeet
collection PubMed
description BACKGROUND: Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning. The global community has recently witnessed an increase in the mortality rate due to liver disease. This could be attributed to many factors, among which are human habits, awareness issues, poor healthcare, and late detection. To curb the growing threats from liver disease, early detection is critical to help reduce the risks and improve treatment outcome. Emerging technologies such as machine learning, as shown in this study, could be deployed to assist in enhancing its prediction and treatment. AIM: To present a more efficient system for timely prediction of liver disease using a hybrid eXtreme Gradient Boosting model with hyperparameter tuning with a view to assist in early detection, diagnosis, and reduction of risks and mortality associated with the disease. METHODS: The dataset used in this study consisted of 416 people with liver problems and 167 with no such history. The data were collected from the state of Andhra Pradesh, India, through https://www.kaggle.com/datasets/uciml/indian-liver-patient-records. The population was divided into two sets depending on the disease state of the patient. This binary information was recorded in the attribute "is_patient". RESULTS: The results indicated that the chi-square automated interaction detection and classification and regression trees models achieved an accuracy level of 71.36% and 73.24%, respectively, which was much better than the conventional method. The proposed solution would assist patients and physicians in tackling the problem of liver disease and ensuring that cases are detected early to prevent it from developing into cirrhosis (scarring) and to enhance the survival of patients. The study showed the potential of machine learning in health care, especially as it concerns disease prediction and monitoring. CONCLUSION: This study contributed to the knowledge of machine learning application to health and to the efforts toward combating the problem of liver disease. However, relevant authorities have to invest more into machine learning research and other health technologies to maximize their potential.
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spelling pubmed-97828382022-12-24 Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy Dalal, Surjeet Onyema, Edeh Michael Malik, Amit World J Gastroenterol Clinical and Translational Research BACKGROUND: Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning. The global community has recently witnessed an increase in the mortality rate due to liver disease. This could be attributed to many factors, among which are human habits, awareness issues, poor healthcare, and late detection. To curb the growing threats from liver disease, early detection is critical to help reduce the risks and improve treatment outcome. Emerging technologies such as machine learning, as shown in this study, could be deployed to assist in enhancing its prediction and treatment. AIM: To present a more efficient system for timely prediction of liver disease using a hybrid eXtreme Gradient Boosting model with hyperparameter tuning with a view to assist in early detection, diagnosis, and reduction of risks and mortality associated with the disease. METHODS: The dataset used in this study consisted of 416 people with liver problems and 167 with no such history. The data were collected from the state of Andhra Pradesh, India, through https://www.kaggle.com/datasets/uciml/indian-liver-patient-records. The population was divided into two sets depending on the disease state of the patient. This binary information was recorded in the attribute "is_patient". RESULTS: The results indicated that the chi-square automated interaction detection and classification and regression trees models achieved an accuracy level of 71.36% and 73.24%, respectively, which was much better than the conventional method. The proposed solution would assist patients and physicians in tackling the problem of liver disease and ensuring that cases are detected early to prevent it from developing into cirrhosis (scarring) and to enhance the survival of patients. The study showed the potential of machine learning in health care, especially as it concerns disease prediction and monitoring. CONCLUSION: This study contributed to the knowledge of machine learning application to health and to the efforts toward combating the problem of liver disease. However, relevant authorities have to invest more into machine learning research and other health technologies to maximize their potential. Baishideng Publishing Group Inc 2022-12-14 2022-12-14 /pmc/articles/PMC9782838/ /pubmed/36569269 http://dx.doi.org/10.3748/wjg.v28.i46.6551 Text en ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved. https://creativecommons.org/licenses/by-nc/4.0/This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
spellingShingle Clinical and Translational Research
Dalal, Surjeet
Onyema, Edeh Michael
Malik, Amit
Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy
title Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy
title_full Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy
title_fullStr Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy
title_full_unstemmed Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy
title_short Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy
title_sort hybrid xgboost model with hyperparameter tuning for prediction of liver disease with better accuracy
topic Clinical and Translational Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782838/
https://www.ncbi.nlm.nih.gov/pubmed/36569269
http://dx.doi.org/10.3748/wjg.v28.i46.6551
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