Obstructive Sleep Apnea Severity Prediction Model GUI using Anthropometrics

International Journal of Electrical and Electronics Engineering
© 2022 by SSRG - IJEEE Journal
Volume 9 Issue 12
Year of Publication : 2022
Authors : S. Aswath, S. Valarmathi
How to Cite?

S. Aswath, S. Valarmathi, "Obstructive Sleep Apnea Severity Prediction Model GUI using Anthropometrics," SSRG International Journal of Electrical and Electronics Engineering, vol. 9,  no. 12, pp. 134-144, 2022. Crossref, https://doi.org/10.14445/23488379/IJEEE-V9I12P112


It is still challenging to anticipate the austerity of obstructive sleep apnea (OSA) in clinical practice. Polysomnography (PSG) has been widely used for predicting OSA, but it is expensive. To tackle the challenge, in this work, a Machine Learning (ML) approach was used to create a predictive model for determining OSA severity level, and a web application was created based on the ML model, where anyone can check their level of OSA. Instead of PSG, anthropometrics such as weight, height, waist circumference, head, etc., can be used to predict OSA severity. Moreover, these parameters are very cheap and easy to measure. To validate this methodology, 5245 records from the TMUH (Taipei Medical University Hospital) sleep center dataset are employed, which has the physical body characteristics along with the age and gender of the patients. To evaluate the model, supervised machine learning classifiers were implemented to predict the OSA severity. Random Forest classifier performed well on processed data with an accuracy of 91%. With the help of a random forest model pickle file, a web application has been developed to classify the OSA severity based on anthropometrics.


Anthropometrics, Obstructive sleep apnea, Polysomnography, Supervised machine learning classifier, Web application.


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