Predicting the severity of obstructive sleep apnea using artificial intelligence tools
Barış Çil, Halit Irmak, Mehmet KabakABSTRACT
BACKGROUND:
We developed an artificial intelligence (AI) model to predict the severity of obstructive sleep apnea syndrome (OSAS).
METHODS:
We used data from 750 inpatients at a research hospital between 2021 and 2023. The dataset comprises 20 attributes, including demographic information, medical history, anthropometric measurements, and polysomnography (PSG) data. The target attribute was the apnea–hypopnea Index (AHI), from which OSAS severity was determined. Data preprocessing included min–max scaling for normalization and the Synthetic Minority Over-sampling Technique algorithm to address the class imbalance, increasing the dataset size to 1250. We invented and further developed a multilayer artificial neural network (ANN) model to predict OSAS severity and evaluated its performance using k-fold cross-validation. We also performed an information gain analysis to rank the features by importance.
RESULTS:
The ANN model accurately predicted OSAS severity (area under the receiver operating characteristic curve: 0.966, CA: 0.880). Information gain analysis revealed strong associations between OSAS severity and the Epworth Sleepiness Scale, lowest nighttime oxygen saturation, percentage of sleep time with oxygen saturation between 80% and 90% during the night, and neck thickness. These identified features represent important risk factors for early OSAS diagnosis and treatment.
CONCLUSION:
Our findings suggest that AI-based models can effectively predict OSAS severity. This research may contribute to the development of next-generation diagnostic tools for OSAS diagnosis and risk assessment. AI can readily determine OSAS severity from overnight pulse oximetry recordings, combined with other risk factors, in patients with suspected OSAS.