Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project

This study evaluates and compares the performance of different machine learning techniques on predicting the individuals at risk of developing hypertension, and who are likely to benefit most from interventions, using the cardiorespiratory fitness data. The dataset of this study contains information...

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Published in:	PloS one Vol. 13; no. 4; p. e0195344
Main Authors:	Sakr, Sherif, Elshawi, Radwa, Ahmed, Amjad, Qureshi, Waqas T., Brawner, Clinton, Keteyian, Steven, Blaha, Michael J., Al-Mallah, Mouaz H.
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 18.04.2018 Public Library of Science (PLoS)
Subjects:	Adolescent Adult Aged Aged, 80 and over Algorithms Analysis Area Under Curve Artificial intelligence Artificial neural networks Bayes Theorem Bayesian analysis Biology and Life Sciences Cardiorespiratory fitness Cardiorespiratory Fitness - physiology Cardiovascular disease Computer and Information Sciences Databases, Factual Ecology and Environmental Sciences Electronic health records Exercise Exercise Test - methods Family medical history Female Fitness equipment Health sciences Heart Hospital systems Hospitals Humans Hypertension Hypertension - etiology International conferences Learning algorithms Learning theory Machine Learning Male Mathematical models Medical records Medical research Medicine and Health Sciences Middle Aged Model accuracy Neural networks Neural Networks, Computer Optimization techniques Patients Physical fitness Physical Sciences Predictions Research and Analysis Methods Studies Support Vector Machine Support vector machines Variables Young Adult Saudi Arabia Detroit Michigan United States > US Riyadh Saudi Arabia Estonia
ISSN:	1932-6203, 1932-6203
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Abstract	This study evaluates and compares the performance of different machine learning techniques on predicting the individuals at risk of developing hypertension, and who are likely to benefit most from interventions, using the cardiorespiratory fitness data. The dataset of this study contains information of 23,095 patients who underwent clinician- referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 10-year follow-up. The variables of the dataset include information on vital signs, diagnosis and clinical laboratory measurements. Six machine learning techniques were investigated: LogitBoost (LB), Bayesian Network classifier (BN), Locally Weighted Naive Bayes (LWB), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Tree Forest (RTF). Using different validation methods, the RTF model has shown the best performance (AUC = 0.93) and outperformed all other machine learning techniques examined in this study. The results have also shown that it is critical to carefully explore and evaluate the performance of the machine learning models using various model evaluation methods as the prediction accuracy can significantly differ.
AbstractList	This study evaluates and compares the performance of different machine learning techniques on predicting the individuals at risk of developing hypertension, and who are likely to benefit most from interventions, using the cardiorespiratory fitness data. The dataset of this study contains information of 23,095 patients who underwent clinician- referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 10-year follow-up. The variables of the dataset include information on vital signs, diagnosis and clinical laboratory measurements. Six machine learning techniques were investigated: LogitBoost (LB), Bayesian Network classifier (BN), Locally Weighted Naive Bayes (LWB), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Tree Forest (RTF). Using different validation methods, the RTF model has shown the best performance (AUC = 0.93) and outperformed all other machine learning techniques examined in this study. The results have also shown that it is critical to carefully explore and evaluate the performance of the machine learning models using various model evaluation methods as the prediction accuracy can significantly differ. This study evaluates and compares the performance of different machine learning techniques on predicting the individuals at risk of developing hypertension, and who are likely to benefit most from interventions, using the cardiorespiratory fitness data. The dataset of this study contains information of 23,095 patients who underwent clinician- referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 10-year follow-up. The variables of the dataset include information on vital signs, diagnosis and clinical laboratory measurements. Six machine learning techniques were investigated: LogitBoost (LB), Bayesian Network classifier (BN), Locally Weighted Naive Bayes (LWB), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Tree Forest (RTF). Using different validation methods, the RTF model has shown the best performance (AUC = 0.93) and outperformed all other machine learning techniques examined in this study. The results have also shown that it is critical to carefully explore and evaluate the performance of the machine learning models using various model evaluation methods as the prediction accuracy can significantly differ.This study evaluates and compares the performance of different machine learning techniques on predicting the individuals at risk of developing hypertension, and who are likely to benefit most from interventions, using the cardiorespiratory fitness data. The dataset of this study contains information of 23,095 patients who underwent clinician- referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 10-year follow-up. The variables of the dataset include information on vital signs, diagnosis and clinical laboratory measurements. Six machine learning techniques were investigated: LogitBoost (LB), Bayesian Network classifier (BN), Locally Weighted Naive Bayes (LWB), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Tree Forest (RTF). Using different validation methods, the RTF model has shown the best performance (AUC = 0.93) and outperformed all other machine learning techniques examined in this study. The results have also shown that it is critical to carefully explore and evaluate the performance of the machine learning models using various model evaluation methods as the prediction accuracy can significantly differ.
Audience	Academic
Author	Qureshi, Waqas T. Blaha, Michael J. Elshawi, Radwa Al-Mallah, Mouaz H. Sakr, Sherif Ahmed, Amjad Brawner, Clinton Keteyian, Steven
AuthorAffiliation	2 King Abdullah International Medical Research Center, Riyadh, Saudia Arabia Northeast Normal University, CHINA 3 Heart and Vascular Institute, Henry Ford Hospital System, Detroit, MI, United States of America 6 Johns Hopkins Medicine, Baltimore, Maryland, United States of America 1 King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia 5 Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, United States of America 4 Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia 7 University of Taru, Taru, Estonia
AuthorAffiliation_xml	– name: 3 Heart and Vascular Institute, Henry Ford Hospital System, Detroit, MI, United States of America – name: 7 University of Taru, Taru, Estonia – name: 5 Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, United States of America – name: 1 King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia – name: 2 King Abdullah International Medical Research Center, Riyadh, Saudia Arabia – name: 4 Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia – name: Northeast Normal University, CHINA – name: 6 Johns Hopkins Medicine, Baltimore, Maryland, United States of America
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Copyright	COPYRIGHT 2018 Public Library of Science 2018 Sakr et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2018 Sakr et al 2018 Sakr et al
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SubjectTerms	Adolescent Adult Aged Aged, 80 and over Algorithms Analysis Area Under Curve Artificial intelligence Artificial neural networks Bayes Theorem Bayesian analysis Biology and Life Sciences Cardiorespiratory fitness Cardiorespiratory Fitness - physiology Cardiovascular disease Computer and Information Sciences Databases, Factual Ecology and Environmental Sciences Electronic health records Exercise Exercise Test - methods Family medical history Female Fitness equipment Health sciences Heart Hospital systems Hospitals Humans Hypertension Hypertension - etiology International conferences Learning algorithms Learning theory Machine Learning Male Mathematical models Medical records Medical research Medicine and Health Sciences Middle Aged Model accuracy Neural networks Neural Networks, Computer Optimization techniques Patients Physical fitness Physical Sciences Predictions Research and Analysis Methods Studies Support Vector Machine Support vector machines Variables Young Adult
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Title	Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project
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