A support vector machine model predicted severe obstructive sleep apnea with an accuracy of 0.86 (sensitivity 0.93, specificity 0.80), outperforming a logistic regression model (accuracy 0.68).
Observational (n=498)
Does a support vector machine model improve the prediction of obstructive sleep apnea severity compared to logistic regression in patients with clinical suspicion of OSA?
An artificial intelligence support vector machine model outperformed traditional logistic regression in identifying severe obstructive sleep apnea risk based on clinical features.
Absolute Event Rate: 0.86% vs 0.68%
OBJECTIVES: To evaluate the role of clinical scores assessing the risk of disease severity in patients with clinical suspicion of obstructive sleep apnea syndrome (OSA). The hypothesis was tested by applying artificial intelligence (AI) to demonstrate its effectiveness in distinguishing between mild-moderate OSA and severe OSA risk. METHODS: A support vector machine model (SVM) was developed from the samples included in the analysis (N = 498), and they were split into 75% for training (N = 373) with the remaining for testing (N = 125). Two diagnostic thresholds were selected for OSA severity: mild to moderate (apnea-hypopnea index (AHI) ≥ 5 events/h and AHI < 30 events/h) and severe (AHI ≥ 30 events/h). The algorithms were trained and tested to predict OSA patient severity. RESULTS: The sensitivity and specificity for the SVM model were 0.93 and 0.80 with an accuracy of 0.86; instead, the logistic regression full mode reported a value of 0.74 and 0.63, respectively, with an accuracy of 0.68. After backward stepwise elimination for features selection, the reduced logistic regression model demonstrated a sensitivity and specificity of 0.79 and 0.56, respectively, and an accuracy of 0.67. CONCLUSION: Artificial intelligence could be applied to patients with symptoms related to OSA to identify individuals with a severe OSA risk with clinical-based algorithms in the OSA framework.
Maniaci et al. (Sun,) conducted a observational in Obstructive sleep apnea syndrome (OSA) (n=498). Support vector machine model (SVM) vs. Logistic regression model was evaluated on Prediction of OSA patient severity (mild-moderate vs severe). A support vector machine model predicted severe obstructive sleep apnea with an accuracy of 0.86 (sensitivity 0.93, specificity 0.80), outperforming a logistic regression model (accuracy 0.68).
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