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 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Oct2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145758127&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145758127 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Oct2020 vid: 58 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145758127 145758127 NLM32803448 145758127 10.1007/s11517-020-02206-9 NLM32803448 145758127 ppf: 2517 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A comparison of regularized logistic regression and random forest machine learning models for daytime diagnosis of obstructive sleep apnea. aug: au: Hajipour, Farahnaz Jozani, Mohammad Jafari Moussavi, Zahra affil: Biomedical Engineering Program, University of Manitoba, Winnipeg, Canada sug: subj: Logistic Regression Sleep Apnea, Obstructive Diagnosis Diagnosis, Computer Assisted Methods Aged Wakefulness Adult Adolescence Middle Age Male Human Young Adult Resource Databases Signal Processing, Computer Assisted Breath Tests Methods Female Trachea Comparative Studies Multicenter Studies Evaluation Research Validation Studies Arthritis Impact Measurement Scales Aged: 65+ years Adult: 19-44 years Adolescent: 13-18 years Middle Aged: 45-64 years Male Female ab: 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 techniques to classify subjects with obstructive sleep apnea (OSA) using their daytime tracheal breathing sounds. We evaluate and compare the performance of the random forest (RF) and regularized logistic regression (LR) as feature selection tools and classification approaches for wakefulness OSA screening. Results show that the RF, which is a low-variance committee-based approach, outperforms the regularized LR in terms of blind-testing accuracy, specificity, and sensitivity with 3.5%, 2.4%, and 3.7% improvement, respectively. However, the regularized LR was found to be faster than the RF and resulted in a more parsimonious model. Consequently, both the RF and regularized LR feature reduction and classification approaches are qualified to be applied for the daytime OSA screening studies, depending on the nature of data and applications' purposes. Graphical Abstract. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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