Aditif Bayes Ağları Yöntemi ile Obstrüktif Uyku Apnesi Şiddetinin Tahminlenmesi

Öz

Amaç: Günümüzde makine öğrenmesi temelli tahmin modelleri, birçok farklı alanda yaygın bir şekilde kullanılmaktadır. Bu çalışmanın amacı obstrüktif uyku apnesi OUA şiddetinin tahminlenmesinde hastaların klinik özelliklerini içeren bir aditif bayes ağ modeli geliştirilmesidir.Gereç ve Yöntemler: Bu çalışmaya Ocak 2014-Ağustos 2015 tarihleri arasında Akdeniz Üniversitesi Kulak-Burun-Boğaz Hastalıkları Anabilim Dalı’nda OUA ön tanısı ile polisomnografi yapılmış 338 hasta dahil edilmiştir. OUA şiddeti ile ilişkili tüm klinik değişkenler ve bu değişkenlerin birbirleriyle olan ilişkileri aditif bayes ağ modeli ile modellenmiştir.Bulgular: Nihai Bayes modelindeki en önemli öngördürücü değişkenler, yumuşak damak ve uvula boyutu Odds oranı OR :10.94 [1.87 - 64.11] ile mallampati skoru OR: 4.5 [1.46 - 43.22] idi. Nihai modelin 10 kat çapraz geçerlilik test sonucu, OUA şiddetinin 0.632 duyarlılık ve 0.529 seçicilik ile tahminlenebileceğini gösterdi. Ayrıca, geliştirilen modelin ağır OUA hastalarını sınıflandırmada 0.777 duyarlılık ve 0.646 seçiciliğe sahip olduğu saptandı. Sonuç: Makine öğrenmesi temelli tahmin modelleri, klinik değişkenler arasındaki kompleks ilişkileri analiz ederek OUA tanısı ve şiddetinin tahmin edilmesini kolaylaştırabilir

Anahtar Kelimeler

• Aditif Bayes Ağları, Obstruktif uyku apnesi, Klinik karar destek sistemleri

Estimation of Obstructive Sleep Apnea Severity Using Additive Bayesian Networks

Öz

Objective: Currently, machine learning-based prediction models have been widely used in many different areas. The aim of this study is to develop an additive bayesian network model including characteristics of patients in order to estimate severity of obstructive sleep apnea OSA .Material and Methods: A total of 338 patients who underwent polysomnography due to prediagnosis of OSA at Akdeniz University Department of Otorhinolaryngology between January 2014 and August 2015 were enrolled to this study. All clinical variables related to severity of OSA, and relationships of these variables among each other were modelled by the additive Bayesian network model.Results: The most important predictor variables in the final Bayesian model were the size of soft palate and uvula Odds ratio OR :10.94 [1.87 - 64.11] , and Mallampati score OR: 4.5 [1.46 43.22] . The result of 10-fold cross-validated test for final model indicated that the severity of OSA can be estimated with a sensitivity of 0.632 and specificity of 0.529. In addition, it was determined that the developed model has 0.777 sensitivity and 0.646 specificity to classify severe OSA patients.Conclusion: Machine learning-based prediction models may facilitate the estimation of diagnosis of OSA and its severity by analyzing complex relationships between clinical variables

Anahtar Kelimeler

• Additive Bayesian Network, Obstructive Sleep Apnea, Clinical Decision Support Systems

Kaynakça

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