Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models

<italic>Objective:</italic> Chronic diseases have become the most prevalent and costly health conditions in the healthcare industry, deteriorating the quality of life, adversely affecting the work productivity, and costing astounding medical resources. However, few studies have been cond...

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Autores principales:	Jingmei Yang, Xinglong Ju, Feng Liu, Onur Asan, Timothy Church, Jeff Smith
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Multiple chronic conditions machine learning predictive analysis health informatics Computer applications to medicine. Medical informatics R858-859.7 Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/dd02874a4f8c4f0f96666300474ee351
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id	oai:doaj.org-article:dd02874a4f8c4f0f96666300474ee351
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spelling	oai:doaj.org-article:dd02874a4f8c4f0f96666300474ee3512021-11-24T00:03:44ZPrediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models2644-127610.1109/OJEMB.2021.3117872https://doaj.org/article/dd02874a4f8c4f0f96666300474ee3512021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9560038/https://doaj.org/toc/2644-1276<italic>Objective:</italic> Chronic diseases have become the most prevalent and costly health conditions in the healthcare industry, deteriorating the quality of life, adversely affecting the work productivity, and costing astounding medical resources. However, few studies have been conducted on the predictive analysis of multiple chronic conditions (MCC) based on the working population. <italic>Results:</italic> Seven machine learning algorithms are used to support the decision making of healthcare practitioner on the risk of MCC. The models were developed and validated using checkup data from 451,425 working population collected by the healthcare providers. Our result shows that all proposed models achieved satisfactory performance, with the AUC values ranging from 0.826 to 0.850. Among the seven predictive models, the gradient boosting tree model outperformed other models, achieving an AUC of 0.850. <italic>Conclusions:</italic> Our risk prediction model shows great promise in automating real-time diagnosis, supporting healthcare practitioners to target high-risk individuals efficiently, and helping healthcare practitioners tailor proactive strategies to prevent the onset or delay the progression of the chronic diseases.Jingmei YangXinglong JuFeng LiuOnur AsanTimothy ChurchJeff SmithIEEEarticleMultiple chronic conditionsmachine learningpredictive analysishealth informaticsComputer applications to medicine. Medical informaticsR858-859.7Medical technologyR855-855.5ENIEEE Open Journal of Engineering in Medicine and Biology, Vol 2, Pp 291-298 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Multiple chronic conditions machine learning predictive analysis health informatics Computer applications to medicine. Medical informatics R858-859.7 Medical technology R855-855.5
spellingShingle	Multiple chronic conditions machine learning predictive analysis health informatics Computer applications to medicine. Medical informatics R858-859.7 Medical technology R855-855.5 Jingmei Yang Xinglong Ju Feng Liu Onur Asan Timothy Church Jeff Smith Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models
description	<italic>Objective:</italic> Chronic diseases have become the most prevalent and costly health conditions in the healthcare industry, deteriorating the quality of life, adversely affecting the work productivity, and costing astounding medical resources. However, few studies have been conducted on the predictive analysis of multiple chronic conditions (MCC) based on the working population. <italic>Results:</italic> Seven machine learning algorithms are used to support the decision making of healthcare practitioner on the risk of MCC. The models were developed and validated using checkup data from 451,425 working population collected by the healthcare providers. Our result shows that all proposed models achieved satisfactory performance, with the AUC values ranging from 0.826 to 0.850. Among the seven predictive models, the gradient boosting tree model outperformed other models, achieving an AUC of 0.850. <italic>Conclusions:</italic> Our risk prediction model shows great promise in automating real-time diagnosis, supporting healthcare practitioners to target high-risk individuals efficiently, and helping healthcare practitioners tailor proactive strategies to prevent the onset or delay the progression of the chronic diseases.
format	article
author	Jingmei Yang Xinglong Ju Feng Liu Onur Asan Timothy Church Jeff Smith
author_facet	Jingmei Yang Xinglong Ju Feng Liu Onur Asan Timothy Church Jeff Smith
author_sort	Jingmei Yang
title	Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models
title_short	Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models
title_full	Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models
title_fullStr	Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models
title_full_unstemmed	Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models
title_sort	prediction for the risk of multiple chronic conditions among working population in the united states with machine learning models
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/dd02874a4f8c4f0f96666300474ee351
work_keys_str_mv	AT jingmeiyang predictionfortheriskofmultiplechronicconditionsamongworkingpopulationintheunitedstateswithmachinelearningmodels AT xinglongju predictionfortheriskofmultiplechronicconditionsamongworkingpopulationintheunitedstateswithmachinelearningmodels AT fengliu predictionfortheriskofmultiplechronicconditionsamongworkingpopulationintheunitedstateswithmachinelearningmodels AT onurasan predictionfortheriskofmultiplechronicconditionsamongworkingpopulationintheunitedstateswithmachinelearningmodels AT timothychurch predictionfortheriskofmultiplechronicconditionsamongworkingpopulationintheunitedstateswithmachinelearningmodels AT jeffsmith predictionfortheriskofmultiplechronicconditionsamongworkingpopulationintheunitedstateswithmachinelearningmodels
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Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models

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