Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project

Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project

Abstract Background Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of this s...

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Bibliographic Details
Main Authors: Sherif Sakr, Radwa Elshawi, Amjad M. Ahmed, Waqas T. Qureshi, Clinton A. Brawner, Steven J. Keteyian, Michael J. Blaha, Mouaz H. Al-Mallah
Format: Article
Language:English
Published: BMC 2017-12-01
Series:BMC Medical Informatics and Decision Making
Subjects:
FIT (Henry ford ExercIse testing) project
All-cause mortality
Machine learning
Online Access:http://link.springer.com/article/10.1186/s12911-017-0566-6

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http://link.springer.com/article/10.1186/s12911-017-0566-6

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