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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

This study addresses Obstructive Sleep Apnea (OSA), which impacts around 936 million adults globally. The research introduces a novel decision support method named Communalities on Ranking and Objective Weights Method (CROWM), which employs principal component analysis (PCA), unsupervised Machine Learning technique, and Multicriteria Decision Analysis (MCDA) to calculate performance criteria weights of Continuous Positive Airway Pressure (CPAP—key in managing OSA) and to evaluate these devices. Uniquely, the CROWM incorporates non-beneficial criteria in PCA and employs communalities to accurately represent the performance evaluation of alternatives within each resulting principal factor, allowing for a more accurate and robust analysis of alternatives and variables. This article aims to employ CROWM to evaluate CPAP for effectiveness in combating OSA, considering six performance criteria: resources, warranty, noise, weight, cost, and maintenance. Validated by established tests and sensitivity analysis against traditional methods, CROWM proves its consistency, efficiency, and superiority in decision-making support. This method is poised to influence assertive decision-making significantly, aiding healthcare professionals, researchers, and patients in selecting optimal CPAP solutions, thereby advancing patient care in an interdisciplinary research context.

Details

Title
Optimization of Obstructive Sleep Apnea Management: Novel Decision Support via Unsupervised Machine Learning
Author
Arthur Pinheiro de Araújo Costa 1   VIAFID ORCID Logo  ; Terra, Adilson Vilarinho 2 ; Claudio de Souza Rocha Junior 2 ; Igor Pinheiro de Araújo Costa 2   VIAFID ORCID Logo  ; Miguel Ângelo Lellis Moreira 2 ; Marcos dos Santos 1   VIAFID ORCID Logo  ; Simões Gomes, Carlos Francisco 2 ; da Silva, Antonio Sergio 2   VIAFID ORCID Logo 

 Systems and Computing, Military Institute of Engineering (IME), Rio de Janeiro 22290-270, RJ, Brazil; [email protected] 
 Operational Research, Fluminense Federal University (UFF), Niterói 24210-346, RJ, Brazil; [email protected] (A.V.T.); [email protected] (C.d.S.R.J.); [email protected] (I.P.d.A.C.); [email protected] (M.Â.L.M.); [email protected] (C.F.S.G.); [email protected] (A.S.d.S.) 
First page
22
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
22279709
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3072344146
Copyright
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.