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수면호흡장애 환자의 인공지능 기반 심혈관질환 예측 모델
박종욱(Jong-Uk Park),류지승(Ji-Seung Rye),강승영(Seung-Young Kang),김윤지(Yun-Ji Kim),김이웅(Yeewoong Kim),이경중(Kyoung-Joung Lee) 대한전자공학회 2020 대한전자공학회 학술대회 Vol.2020 No.8
This study proposes a method of prediction of cardiovascular disease (CVD) that can develop within ten years in patients with sleep-disordered breathing (SDB). From the data during a baseline period when patients did not have any CVD, we extracted 18 features from electrography (ECG) based on signal processing methods, 30 ECG features based on artificial intelligence (AI), ten clinical risk factors for CVD. We trained the model and evaluated it by using CVD outcomes result, monitored in follow-ups. The optimal feature vectors were selected through statistical analysis and support vector machine recursive feature elimination (SVM-RFE) of the extracted feature vectors. Features based on AI, a novel proposal from this study, showed excellent performance out of all selected feature vectors. Also, new parameters based on AI were possibly meaningful predictors for CVD, when used in addition to the predictors for CVD that are already known. The selected features were used as inputs to the prediction model based on SVM. As a result, the respective recall and precision values were 82.9% and 87.5% for CVD-free. The F1-score between CVD and CVD-free was 76.5. In conclusion, our results confirm the excellence of the prediction model for CVD in patients with SDB and verify the possibility of prediction within ten years of the CVD that may occur in patients with SDB.
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