Title: A machine learning-based methodology for stratifying patients into obstructive sleep apnoea risk

Authors: Christos Bellos; Konstantinos Stefanou; Georgios Stergios; Dafni Patelou; Thomas Katsantas; Konstantinos P. Exarchos; Apostolis Nikolopoulos; Agni Sioutkou; Georgios Siopis; Konstantinos Kostikas; Chara Tselepi; Athanasios Konstantinidis

Addresses: Lime Technology IKE, Archiepiskopou Makariou, Ioannina, 45221, Greece ' Lime Technology IKE, Archiepiskopou Makariou, Ioannina, 45221, Greece ' Lime Technology IKE, Archiepiskopou Makariou, Ioannina, 45221, Greece ' Lime Technology IKE, Archiepiskopou Makariou, Ioannina, 45221, Greece ' Lime Technology IKE, Archiepiskopou Makariou, Ioannina, 45221, Greece ' University of Ioannina, P.O. Box 1186, 45110 Ioannina, Greece ' University of Ioannina, P.O. Box 1186, 45110 Ioannina, Greece ' University of Ioannina, P.O. Box 1186, 45110 Ioannina, Greece ' University of Ioannina, P.O. Box 1186, 45110 Ioannina, Greece ' University of Ioannina, P.O. Box 1186, 45110 Ioannina, Greece ' University of Ioannina, P.O. Box 1186, 45110 Ioannina, Greece ' University of Ioannina, P.O. Box 1186, 45110 Ioannina, Greece

Abstract: Obstructive sleep apnoea (OSA) is a common and chronic disorder that leads to increased day-time sleepiness, is associated with accidents, emerging of cardiovascular and metabolic disorders as well as depression. Almost 20% of the population suffers from OSA while a large portion of people are undiagnosed. The objectives of the current work are: 1) the development of a platform to keep a record of home sleep studies and monitor patients; 2) its use for screening tool for the detection of undiagnosed cases in the general population; 3) the design of a machine learning-based methodology for stratifying patients into high and low risk of OSA based on a series of clinical findings and questionnaires. The proposed methodology showed overall accuracy 87.4%, sensitivity 92.1% and specificity 77.1%.

Keywords: obstructive sleep apnoea; machine learning; web-based system; visualisation platform; data analysis.

DOI: 10.1504/IJMEI.2025.147592

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.4, pp.360 - 369

Received: 05 May 2022
Accepted: 19 Nov 2022
Published online: 24 Jul 2025 *

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