Decoding the night: a machine learning approach to diagnose and predict the severity of obstructive sleep apnea through clinical parameters
Year: 2026
Type: article
Abstract: Obstructive sleep apnea (OSA) is a common sleep-related disorder associated with cardiovascular and metabolic comorbidities. Early diagnosis is essential; however, access to polysomnography is limited. Machine learning approaches may support clinical decision-making by predicting OSA using easily obtainable variables. This study aimed to develop an artificial neural network (ANN) model to predict the presence and severity of OSA. This multicenter study included adult patients who underwent diagnostic polysomnography at ten different sleep centers. Eighteen demographic and clinical parameters were used as input features, and four OSA severity categories served as output classes. The ANN architecture consisted of one hidden layer with 50 neurons (ReLU activation) and a Softmax output layer. The dataset was randomly divided into training (80%) and testing (20%) sets. A total of 1,954 participants (70.2% male) were included. Polysomnography classified 39.2% of participants as severe OSA, 29.4% as mild OSA, 15.0% as moderate OSA, and 16.4% as non-OSA. The ANN model demonstrated good discriminative performance across OSA severity categories, with class-specific AUC values ranging from 0.89 to 0.96. These findings support the potential utility of ML-based tools for screening and early risk stratification, particularly in resource-limited settings.
Source: Scientific Reports
Authors: Neslihan Özçelik, Emre Bendeş, Ali Erdem Özçelik, Elvan Şentürk Topaloğlu, Songül Özyurt, Mehmet Karadağ, Özge Aydın Güçlü, Merve Nur Yıldız, Hadice Selimoğlu Şen, Nur Aleyna Yetkin, Burcu Baran, Önder Öztürk, Merve Erçelik, Hilal Türkmen Kaya, Nurhan Sarıoğlu, Mustafa Çolak, Muhammed Emin Akkoyunlu, Abdullah Kansu, Abdurrahman Kotan, Fatma Gülsüm Karakaş, Yılmaz Bülbül, Sibel Doğru, Aziz Gümüş, Gizem Özçıbık Işık, Ömer Topaloğlu, Bilge Yılmaz Kara, Hasan Veysel Keskin, Ünal Şahin
Institutions: Recep Tayyip Erdoğan University, Nevşehir Hacı Bektaş Veli University, Bursa Uludağ Üni̇versi̇tesi̇, Dicle University, Erciyes University, Süleyman Demirel Üniversitesi, Balıkesir University, Istanbul Medipol University, Karadeniz Technical University, Gaziantep University, Yedikule Teaching Hospital
Language: English
Subfield: Physiology (medical)
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7225647276
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