Cargando…
Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project
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 study is...
Autores principales: | Sakr, Sherif, Elshawi, Radwa, Ahmed, Amjad M., Qureshi, Waqas T., Brawner, Clinton A., Keteyian, Steven J., Blaha, Michael J., Al-Mallah, Mouaz H. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5735871/ https://www.ncbi.nlm.nih.gov/pubmed/29258510 http://dx.doi.org/10.1186/s12911-017-0566-6 |
Ejemplares similares
-
Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project
por: Sakr, Sherif, et al.
Publicado: (2018) -
Systolic Blood Pressure Response During Exercise Stress Testing: The Henry Ford ExercIse Testing (FIT) Project
por: O’Neal, Wesley T, et al.
Publicado: (2015) -
Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project
por: Alghamdi, Manal, et al.
Publicado: (2017) -
Physical Fitness and Hypertension in a Population at Risk for Cardiovascular Disease: The Henry Ford ExercIse Testing (FIT) Project
por: Juraschek, Stephen P., et al.
Publicado: (2014) -
Higher cardiorespiratory fitness predicts long-term survival in patients with heart failure and preserved ejection fraction: the Henry Ford Exercise Testing (FIT) Project
por: Orimoloye, Olusola A., et al.
Publicado: (2019)