지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.3
- 수록면
- 346 - 355 (10page)
- DOI
- 10.7840/kics.2024.49.3.346
이용수
초록· 키워드
오류제보하기
Chronic Obstructive Pulmonary Disease (COPD) is a serious lung disease that makes breathing difficult and cannot be easily detected. Even though early diagnosis technology for COPD using machine learning has been developed, Pulmonary Function Test (PFT) data-based time series prediction studies are still lacking. We use PFT data with insufficient measurement intervals, propose a Long Short-Term Memory (LSTM) to predict PFT values for the future 1Q from the past 2Q, and classify whether COPD occurs or not. The data were interpolated to resolve the imbalanced time period. To confirm the validity of the augmented data, Multivariate Analysis of Variance (MANOVA) was performed, and through the rigorous MANOVA, we proved that there was no significant difference between the original and interpolated data. Mean Absolute Percentage Error (MAPE), recalls, and F1 scores, which are the harmonic mean of precision and recall for classification, were measured for two test scenarios: only the original data and the augmented data. Finally, we found the interpolated data decreased MAPE by almost 7%, however, improved recall and F1 score by almost 22% and 12% for obstructive pulmonary disease, compared with the original data. Besides, we can predict COPD within 3 months, irrelevant to smokers and non-smokers.
목차
ABSTRACT
Ⅰ. Introduction
Ⅱ. Related Works
Ⅲ. System Model
Ⅳ. Experimental Results
Ⅴ. Conclusion
References
참고문헌 (14)
참고문헌 신청
D. D. Sin, “The importance of early chronic obstructive pulmonary disease: A lecture from 2022 asian pacific society of respirology,” Tuberculosis and Respiratory Diseases, vol. 86, no. 2, pp. 71-81, Apr. 2023. (https://doi.org/10.4046/trd.2023.0005)
H. Park, et al., “Deep learning-based approach to predict pulmonary function at chest CT,” Radiology, vol. 307, no. 2, pp. 221488, Apr. 2023. (https://doi.org/10.1148/radiol.221488)
S. Gonem, et al., “Applications of artificial intelligence and machine learning in respiratory medicine,” Thorax, vol. 75, no. 8, pp. 695-701, Aug. 2020. (https://doi.org/10.1136/thoraxjnl-2020-214556)
L. Beverin, et al., “Predicting total lung capacity from spirometry: A machine learning approach,” Frontiers in Med., vol. 10, pp. 1174631, May 2023. (https://doi.org/10.3389/fmed.2023.1174631)
D. Spathis and P. Vlamos, “Diagnosing asthma and chronic obstructive pulmonary disease with machine learning,” Health Informatics J., vol. 25, no. 3, pp. 811-827, Sep. 2019. (https://doi.org/10.1177/1460458217723169)
H. Park, et al., “Deep learning-based approach to predict pulmonary function at chest CT,” Radiology, vol. 307, no. 2, pp. 221488, Apr. 2023. (https://doi.org/10.1148/radiol.221488)
S. Gonem, et al., “Applications of artificial intelligence and machine learning in respiratory medicine,” Thorax, vol. 75, no. 8, pp. 695-701, Aug. 2020. (https://doi.org/10.1136/thoraxjnl-2020-214556)
L. Beverin, et al., “Predicting total lung capacity from spirometry: A machine learning approach,” Frontiers in Med., vol. 10, pp. 1174631, May 2023. (https://doi.org/10.3389/fmed.2023.1174631)
D. Spathis and P. Vlamos, “Diagnosing asthma and chronic obstructive pulmonary disease with machine learning,” Health Informatics J., vol. 25, no. 3, pp. 811-827, Sep. 2019. (https://doi.org/10.1177/1460458217723169)
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UCI(KEPA) : I410-151-24-02-089490982