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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Bibliographic Details
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
Online Access:Get full text
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Description
Summary: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.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0195344