Decoding the night: a machine learning approach to diagnose and predict the severity of obstructive sleep apnea through clinical parameters

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.

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Publication Details

Journal
Scientific Reports
Published
2026-11-09
DOI
https://doi.org/10.1038/s41598-026-79860-w
Primary Topic
Obstructive Sleep Apnea Research
Type
article
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article

Decoding the night: a machine learning approach to diagnose and predict the severity of obstructive sleep apnea through clinical parameters

Aziz Gümüş, Fatma Gülsüm Karakaş, Emre Bendeş, Abdurrahman Kotan et al.
Scientific Reports
Obstructive Sleep Apnea Research
article

Decoding the night: a machine learning approach to diagnose and predict the severity of obstructive sleep apnea through clinical parameters

Aziz Gümüş, Fatma Gülsüm Karakaş, Emre Bendeş, Abdurrahman Kotan, Merve Erçelik, Ömer Topaloğlu, Bilge YILMAZ KARA, Ünal Şahin, Elvan Senturk Topaloglu, Abdullah Kansu, Gizem Özçıbık Işık, Burcu Baran Ketencioğlu, Nur Aleyna Yetkin, Songül Özyurt, Ali Erdem Özçelik, Önder Öztürk, Mehmet Karadağ, Hasan Veysel Keskin, Yılmaz Bülbül, Sibel Doğru, Nurhan Sarıoğlu, Muhammed Emin Akkoyunlu, Özge Aydın Güçlü, Neslihan Özçelik, Mustafa Çolak, Hadice Selimoğlu Şen, Merve Nur Yıldız, Hilal Türkmen Kaya
article en

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.

Scientific Reports
Balıkesir University (TR), Dicle University (TR), Nevşehir Hacı Bektaş Veli University (TR), Bursa Uludağ Üni̇versi̇tesi̇ (TR), Recep Tayyip Erdoğan University (TR), Karadeniz Technical University (TR), Süleyman Demirel Üniversitesi (TR), Istanbul Medipol University (TR), Yedikule Teaching Hospital (TR), Gaziantep University (TR), Erciyes University (TR)
Obstructive Sleep Apnea Research
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