Classification of Chronic Obstructive Pulmonary Disease (COPD) Through Respiratory Pattern Analysis
<b>Background:</b> This study proposes a classification system for predicting chronic obstructive pulmonary disease (COPD) patients and non-patients based on image and text data. <b>Method:</b> This study measured the respiratory volume based on thermal images, stored the res...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/3/313 |
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| author | Do-Kyeong Lee Jae-Sung Choi Seong-Jun Choi Min-Hyung Choi Min Hong |
| author_facet | Do-Kyeong Lee Jae-Sung Choi Seong-Jun Choi Min-Hyung Choi Min Hong |
| author_sort | Do-Kyeong Lee |
| collection | DOAJ |
| description | <b>Background:</b> This study proposes a classification system for predicting chronic obstructive pulmonary disease (COPD) patients and non-patients based on image and text data. <b>Method:</b> This study measured the respiratory volume based on thermal images, stored the respiratory data, and derived features related to respiratory patterns, including the total respiratory volume, average distance between expirations, average distance between inspirations, and total respiratory rate. The data for each feature were stored in text format. The four features saved as text were scaled using Z-score normalization and expressed as scores through weighted summation. These scores were compared to a threshold based on the ROC curve values, classifying participants as patients if the score exceeded the threshold and as non-patients if it fell below. <b>Results:</b> The proposed method achieved an accuracy of 82.5%. To validate the proposed approach, precision, recall, and F1-score were utilized, confirming the high classification performance of the model. The results of this study demonstrate the potential for future applications in non-contact medical examinations and diagnoses of respiratory diseases. |
| format | Article |
| id | doaj-art-2bd93890ccc548e39eafa4eb9404182a |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-2bd93890ccc548e39eafa4eb9404182a2025-08-20T02:12:38ZengMDPI AGDiagnostics2075-44182025-01-0115331310.3390/diagnostics15030313Classification of Chronic Obstructive Pulmonary Disease (COPD) Through Respiratory Pattern AnalysisDo-Kyeong Lee0Jae-Sung Choi1Seong-Jun Choi2Min-Hyung Choi3Min Hong4Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of KoreaDepartment of Internal Medicine, Cheonan Hospital, College of Medicine, Soonchunhyang University, Cheonan 31151, Republic of KoreaDepartment of Otolaryngology-Head and Neck Surgery, Cheonan Hospital, College of Medicine, Soonchunhyang University, Cheonan 31151, Republic of KoreaDepartment of Computer Science, Saint Louis University, Louis, MO 63103, USADepartment of Computer Software Engineering, Soonchunhyang University, Asan 31538, Republic of Korea<b>Background:</b> This study proposes a classification system for predicting chronic obstructive pulmonary disease (COPD) patients and non-patients based on image and text data. <b>Method:</b> This study measured the respiratory volume based on thermal images, stored the respiratory data, and derived features related to respiratory patterns, including the total respiratory volume, average distance between expirations, average distance between inspirations, and total respiratory rate. The data for each feature were stored in text format. The four features saved as text were scaled using Z-score normalization and expressed as scores through weighted summation. These scores were compared to a threshold based on the ROC curve values, classifying participants as patients if the score exceeded the threshold and as non-patients if it fell below. <b>Results:</b> The proposed method achieved an accuracy of 82.5%. To validate the proposed approach, precision, recall, and F1-score were utilized, confirming the high classification performance of the model. The results of this study demonstrate the potential for future applications in non-contact medical examinations and diagnoses of respiratory diseases.https://www.mdpi.com/2075-4418/15/3/313thermal cameraimage preprocessingCOPDnon-contact diagnosis |
| spellingShingle | Do-Kyeong Lee Jae-Sung Choi Seong-Jun Choi Min-Hyung Choi Min Hong Classification of Chronic Obstructive Pulmonary Disease (COPD) Through Respiratory Pattern Analysis Diagnostics thermal camera image preprocessing COPD non-contact diagnosis |
| title | Classification of Chronic Obstructive Pulmonary Disease (COPD) Through Respiratory Pattern Analysis |
| title_full | Classification of Chronic Obstructive Pulmonary Disease (COPD) Through Respiratory Pattern Analysis |
| title_fullStr | Classification of Chronic Obstructive Pulmonary Disease (COPD) Through Respiratory Pattern Analysis |
| title_full_unstemmed | Classification of Chronic Obstructive Pulmonary Disease (COPD) Through Respiratory Pattern Analysis |
| title_short | Classification of Chronic Obstructive Pulmonary Disease (COPD) Through Respiratory Pattern Analysis |
| title_sort | classification of chronic obstructive pulmonary disease copd through respiratory pattern analysis |
| topic | thermal camera image preprocessing COPD non-contact diagnosis |
| url | https://www.mdpi.com/2075-4418/15/3/313 |
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