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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Vydáno v:	PloS one Ročník 13; číslo 4; s. e0195344
Hlavní autoři:	Sakr, Sherif, Elshawi, Radwa, Ahmed, Amjad, Qureshi, Waqas T., Brawner, Clinton, Keteyian, Steven, Blaha, Michael J., Al-Mallah, Mouaz H.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 18.04.2018 Public Library of Science (PLoS)
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0195344