A comparison of regularized logistic regression and random forest machine learning models for daytime diagnosis of obstructive sleep apnea.
A major challenge in big and high-dimensional data analysis is related to the classification and prediction of the variables of interest by characterizing the relationships between the characteristic factors and predictors. This study aims to assess the utility of two important machine-learning tech...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 10; pp. 2517 - 2530 |
|---|---|
| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Oct2020
|
| Acceso en línea: | Ver este registro en EBSCOhost |