A machine learning approach for the diagnosis of obstructive sleep apnoea using oximetry, demographic and anthropometric data
Introduction: Obstructive sleep apnoea (OSA) is a serious but underdiagnosed condition. Demand for the gold standard diagnostic polysomnogram (PSG) far exceeds its availability. More efficient diagnostic methods are needed, even in tertiary settings. Machine learning (ML) models have strengths in di...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer – Medknow Publications
2025-04-01
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| Series: | Singapore Medical Journal |
| Subjects: | |
| Online Access: | https://journals.lww.com/10.4103/singaporemedj.SMJ-2022-170 |
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