A Machine Learning-Based Screening Model for Obstructive Sleep Apnea Diagnosis and Severity Prediction
Year: 2025
Type: conference-abstract
Abstract: Introduction: Obstructive sleep apnea syndrome (OSAS) is associated with serious health risks, making early diagnosis and severity assessment essential. This study explores an innovative approach by integrating machine learning with clinical parameters, including sleepiness scale, age, gender, comorbidities, and anthropometric measurements, to diagnose and classify OSAS severity. Methods: This multicenter, retrospective cross-sectional study included patients over 18 years with or without PSG-confirmed OSAS. PSG results were categorized into Stage-0 to Stage-3 based on severity. An artificial neural network (ANN) model was developed with 25 input variables and 4 output classes. The hidden layer contained 50 neurons with RELU activation, while SOFTMAX was used in the output layer. The dataset was split into 80% training and 20% testing. Results: A total of 1,954 participants from 9 centers were included; 70.16% were male (n=1,371). The mean age was 47.51 years. Distribution of sleep stages: 39.15% Stage 3, 29.43% Stage 1, 16.38% Stage 0, and 15.05% Stage 2. The model’s performance was assessed via ROC analysis, yielding an AUC of 0.97, indicating strong predictive ability. PPV was 97.56%, and NPV was 87.11%. Conclusion: The ANN model demonstrated high accuracy in OSAS diagnosis, suggesting potential for clinical application. Additionally, the applicability of this approach in community screening may facilitate more efficient management of healthcare resources.
Source: Sleep Science
Authors: Neslihan Özçelik, Emre Bendeş, Ali Erdem Özçelik, Elvan Şentürk Topaloğlu, Songul Ozyurt, Mehmet Karadağ, Özge Aydın Güçlü, Merve Nur Yıldız, Hadice Selimoğlu Şen, Nur Aleyna Yetkin, Burcu Baran Ketencioğlu, Önder Öztürk, 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 Yilmaz Kara, Ünal Şahin
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4416635748
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