@inbook{ce97732e7dd3482dafa323bfee670f06,
title = "Predicting Sleep-Disordered Breathing Using Deep Learning Algorithms",
abstract = "This paper aims to propose a prediction model for Sleep Disordered Breathing (SDB) in relation to different malocclusions and oral habits with the purpose of aiding clinicians in early screening of patients at risk of developing SDB. The study involved 398 children, 201 girls and 197 boys, aged 3 to 6 years old. Clinical examinations were conducted between February 2021 and November 2022 and included diagnosis of different malocclusions and oral habits. PSQ (Chervin{\textquoteright}s Pediatric Questionnaire) was completed by the parents to assess SDB. Artificial Intelligence (AI) based Convolutional Feed Forward Neural Network (NN) SDB prediction model was created. Utilizing Python deep learning algorithms, a Multi-Layered Perceptron (MLP) was developed for SDB prediction. Out of 27 variables, 8 SDB predictors were identified. According to the NN, SDB predictors are: Angle Class II malocclusion, lower lip biting habit, lateral crossbite without mandibular shift, thumb sucking, male gender, increased overjet, open bite and atypical swallowing. The prediction model has an accuracy rate of 96.1%.",
keywords = "artificial intelligence, deep learning, orthodontics, sleep disordered breathing",
author = "Sara Jasen and Evisi Nastasi and Sara Ghanim",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2023",
doi = "10.1007/978-3-031-42455-7_1",
language = "English",
isbn = "978-3-031-42457-1",
series = "Studies in Big Data",
publisher = "Springer",
pages = "1--9",
booktitle = "Cutting-Edge Business Technologies in the Big Data Era",
address = "Netherlands",
}