Estimation of the Prevalence of Nonalcoholic Fatty Liver Disease in an Adult Population in Northern China Using the Data Mining Approach

TengFei Yang,1 Bo Zhao,2 Dongmei Pei1 1Department of Health Management, Shengjing Hospital of China Medical University, Shenyang, People’s Republic of China; 2Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, People’s Republic of ChinaCorre...

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Autores principales:	Yang T, Zhao B, Pei D
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2021
Materias:	nonalcoholic fatty liver disease j48 algorithm decision tree risk factors Specialties of internal medicine RC581-951
Acceso en línea:	https://doaj.org/article/50224dc183b9442cb763ec37370a453e
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spelling	oai:doaj.org-article:50224dc183b9442cb763ec37370a453e2021-12-02T16:57:48ZEstimation of the Prevalence of Nonalcoholic Fatty Liver Disease in an Adult Population in Northern China Using the Data Mining Approach1178-7007https://doaj.org/article/50224dc183b9442cb763ec37370a453e2021-07-01T00:00:00Zhttps://www.dovepress.com/estimation-of-the-prevalence-of-nonalcoholic-fatty-liver-disease-in-an-peer-reviewed-fulltext-article-DMSOhttps://doaj.org/toc/1178-7007TengFei Yang,1 Bo Zhao,2 Dongmei Pei1 1Department of Health Management, Shengjing Hospital of China Medical University, Shenyang, People’s Republic of China; 2Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, People’s Republic of ChinaCorrespondence: Dongmei PeiDepartment of Health Management, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Heping District, Shenyang, 110004, People’s Republic of ChinaEmail peidm1111@hotmail.comBackground: Nonalcoholic fatty liver disease (NAFLD) is the commonest form of chronic liver disease worldwide and its prevalence is rapidly increasing. Screening and early diagnosis of high-risk groups are important for the prevention and treatment of NAFLD; however, traditional imaging examinations are expensive and difficult to perform on a large scale. This study aimed to develop a simple and reliable predictive model based on the risk factors for NAFLD using a decision tree algorithm for the diagnosis of NAFLD and reduction of healthcare costs.Methods: This retrospective cross-sectional study included 22,819 participants who underwent annual health examinations between January 2019 and December 2019 at Physical Examination Center in Shengjing Hospital of China Medical University. After rigorous data screening, data of 9190 participants were retained in the final dataset for use in the J48 decision tree algorithm for the construction of predictive models. Approximately 66% of these patients (n=6065) were randomly assigned to the training dataset for the construction of the decision tree, while 34% of the patients (n=3125) were assigned to the test dataset to evaluate the performance of the decision tree.Results: The results showed that the J48 decision tree classifier exhibited good performance (accuracy=0.830, precision=0.837, recall=0.830, F-measure=0.830, and area under the curve=0.905). The decision tree structure revealed waist circumference as the most significant attribute, followed by triglyceride levels, systolic blood pressure, sex, age, and total cholesterol level.Conclusion: Our study suggests that a decision tree analysis can be used to screen high-risk individuals for NAFLD. The key attributes in the tree structure can further contribute to the prevention of NAFLD by suggesting implementable targeted community interventions, which can help improve the outcome of NAFLD and reduce the burden on the healthcare system.Keywords: nonalcoholic fatty liver disease, J48 algorithm, decision tree, risk factorsYang TZhao BPei DDove Medical Pressarticlenonalcoholic fatty liver diseasej48 algorithmdecision treerisk factorsSpecialties of internal medicineRC581-951ENDiabetes, Metabolic Syndrome and Obesity: Targets and Therapy, Vol Volume 14, Pp 3437-3445 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	nonalcoholic fatty liver disease j48 algorithm decision tree risk factors Specialties of internal medicine RC581-951
spellingShingle	nonalcoholic fatty liver disease j48 algorithm decision tree risk factors Specialties of internal medicine RC581-951 Yang T Zhao B Pei D Estimation of the Prevalence of Nonalcoholic Fatty Liver Disease in an Adult Population in Northern China Using the Data Mining Approach
description	TengFei Yang,1 Bo Zhao,2 Dongmei Pei1 1Department of Health Management, Shengjing Hospital of China Medical University, Shenyang, People’s Republic of China; 2Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, People’s Republic of ChinaCorrespondence: Dongmei PeiDepartment of Health Management, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Heping District, Shenyang, 110004, People’s Republic of ChinaEmail peidm1111@hotmail.comBackground: Nonalcoholic fatty liver disease (NAFLD) is the commonest form of chronic liver disease worldwide and its prevalence is rapidly increasing. Screening and early diagnosis of high-risk groups are important for the prevention and treatment of NAFLD; however, traditional imaging examinations are expensive and difficult to perform on a large scale. This study aimed to develop a simple and reliable predictive model based on the risk factors for NAFLD using a decision tree algorithm for the diagnosis of NAFLD and reduction of healthcare costs.Methods: This retrospective cross-sectional study included 22,819 participants who underwent annual health examinations between January 2019 and December 2019 at Physical Examination Center in Shengjing Hospital of China Medical University. After rigorous data screening, data of 9190 participants were retained in the final dataset for use in the J48 decision tree algorithm for the construction of predictive models. Approximately 66% of these patients (n=6065) were randomly assigned to the training dataset for the construction of the decision tree, while 34% of the patients (n=3125) were assigned to the test dataset to evaluate the performance of the decision tree.Results: The results showed that the J48 decision tree classifier exhibited good performance (accuracy=0.830, precision=0.837, recall=0.830, F-measure=0.830, and area under the curve=0.905). The decision tree structure revealed waist circumference as the most significant attribute, followed by triglyceride levels, systolic blood pressure, sex, age, and total cholesterol level.Conclusion: Our study suggests that a decision tree analysis can be used to screen high-risk individuals for NAFLD. The key attributes in the tree structure can further contribute to the prevention of NAFLD by suggesting implementable targeted community interventions, which can help improve the outcome of NAFLD and reduce the burden on the healthcare system.Keywords: nonalcoholic fatty liver disease, J48 algorithm, decision tree, risk factors
format	article
author	Yang T Zhao B Pei D
author_facet	Yang T Zhao B Pei D
author_sort	Yang T
title	Estimation of the Prevalence of Nonalcoholic Fatty Liver Disease in an Adult Population in Northern China Using the Data Mining Approach
title_short	Estimation of the Prevalence of Nonalcoholic Fatty Liver Disease in an Adult Population in Northern China Using the Data Mining Approach
title_full	Estimation of the Prevalence of Nonalcoholic Fatty Liver Disease in an Adult Population in Northern China Using the Data Mining Approach
title_fullStr	Estimation of the Prevalence of Nonalcoholic Fatty Liver Disease in an Adult Population in Northern China Using the Data Mining Approach
title_full_unstemmed	Estimation of the Prevalence of Nonalcoholic Fatty Liver Disease in an Adult Population in Northern China Using the Data Mining Approach
title_sort	estimation of the prevalence of nonalcoholic fatty liver disease in an adult population in northern china using the data mining approach
publisher	Dove Medical Press
publishDate	2021
url	https://doaj.org/article/50224dc183b9442cb763ec37370a453e
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Estimation of the Prevalence of Nonalcoholic Fatty Liver Disease in an Adult Population in Northern China Using the Data Mining Approach

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