Diagnosis of Obstructive Sleep Apnea Using Logistic Regression and Artificial Neural Networks Models

  • Alaa ShetaEmail author
  • Hamza Turabieh
  • Malik Braik
  • Salim R. Surani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1069)


Regrettably, a large proportion of likely patients with sleep apnea are underdiagnosed. Obstructive sleep apnea (OSA) is one of the main causes of hypertension, type II diabetes, stroke, coronary artery disease, and heart failure. OSA affects not only adults but also children where it forms one of the sources of learning disabilities for children. This study aims to provide a classification model for one of the well-known sleep disorders known as OSA, which causes a serious malady that affects both men and women. OSA affects both genders with different scope. Men versus women diagnosed with OSA are about 8:1. In this research, logistic regression (LR) and artificial neural networks were applied successfully in several classification applications with promising results, particularly in the bio-statistics area. LR was used to derive a membership probability for a potential OSA system from a range of anthropometric features including weight, height, body mass index (BMI), hip, waist, age, neck circumference, modified Friedman, snoring, Epworth sleepiness scale (ESS), sex, and daytime sleepiness. We developed two models to predict OSA, one for men and one for women. The proposed sleep apnea diagnosis model has yielded accurate classification results and possibly a prototype software module that can be used at home. These findings shall reduce the patient’s need to spend a night at a laboratory and make the study of sleep apnea to implement at home.


Sleep apnea Logistic regression Artificial neural networks Classification Features selection 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Alaa Sheta
    • 1
    Email author
  • Hamza Turabieh
    • 2
  • Malik Braik
    • 3
  • Salim R. Surani
    • 4
  1. 1.Computer Science DepartmentSouthern Connecticut State UniversityNew HavenUSA
  2. 2.Department of Information TechnologyTaif UniversityTaifSaudi Arabia
  3. 3.Department of Computer ScienceAl-Balqa Applied UniversitySaltJordan
  4. 4.Department of MedicineTexas A&M UniversityCorpus ChristiUSA

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