A feature reduction and selection algorithm for improved obstructive sleep apnea classification process.
Feature reduction and selection of the best features for classification is a crucial stage for data analysis to reduce training time and overfitting. Commonly, feature reduction techniques rely on selecting features with high relevance to the outcome and minimum mutual information among one another....
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 10; pp. 2063 - 2073 |
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| Autores principales: | , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2021
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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=152447249&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152447249 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Oct2021 vid: 59 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152447249 152073806 152447249 NLM34435275 152447249 10.1007/s11517-021-02421-y NLM34435275 152447249 ppf: 2063 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A feature reduction and selection algorithm for improved obstructive sleep apnea classification process. aug: au: Elwali, Ahmed Moussavi, Zahra affil: Biomedical Engineering, University of Manitoba, Winnipeg, Canada sug: subj: Algorithms Sleep Apnea, Obstructive Diagnosis Human ab: Feature reduction and selection of the best features for classification is a crucial stage for data analysis to reduce training time and overfitting. Commonly, feature reduction techniques rely on selecting features with high relevance to the outcome and minimum mutual information among one another. However, satisfying these criteria does not guarantee the selected features have a high classification power. In this study, we provide an algorithm to build predictive models for the desired outcome, while selecting the most useful features for high classification outcomes. The results of the proposed algorithm are compared with the outcomes of five popular feature reduction and selection techniques. A dataset from an obstructive sleep apnea study (data of 113 and 86 participants as training and blind testing datasets, respectively) was used to illustrate the algorithm's performance. The extracted features used in the algorithm were modeled using three-, four-, and five-feature combinations. The models with a high correlation to the outcome and low overlap percentages among specific subgroups of the data were selected and examined. Then, a set of the best selected models were averaged to provide better classification accuracy. The accuracy of the proposed algorithm has been 25% higher than that when using five popular feature reduction/selection techniques. Furthermore, the proposed algorithm is about 20 times faster than the five popular techniques. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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