Diagnosis of Covid-19 Via Patient Breath Data Using Artificial Intelligence
Yazarlar (7)
Özge Doğuç İstanbul Medipol Üniversitesi, Türkiye
Gökhan Silahtaroğlu İstanbul Medipol Üniversitesi, Türkiye
Zehra Nur Canbolat İstanbul Medipol Üniversitesi, Türkiye
Kailash Hambarde Swami Ramanand Teerth Marathwada University, Hindistan
Ahmet Alperen Yiğitbaşı
İstanbul Medipol Üniversitesi, Türkiye
İstanbul Medipol Üniversitesi, Türkiye
Arş. Gör. Hasan GÖKAY İstanbul Medipol Üniversitesi, Türkiye
Prof. Dr. Mesut Yılmaz İstanbul Medipol Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SCOPUS dergilerinde yayınlanan tam makale)
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| Dergi Adı | Emerging Science Journal | ||
| Dergi ISSN | 2610-9182 Scopus Dergi | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2023 |
| Cilt / Sayı / Sayfa | 7 / 0 / 105–113 | DOI | 10.28991/ESJ-2023-SPER-08 |
| Makale Linki | https://arxiv.org/abs/2302.10180 | ||
| UAK Araştırma Alanları |
Sağlık Bilimleri
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| Özet |
| Using machine learning algorithms for the rapid diagnosis and detection of the COVID-19 pandemic and isolating the patients from crowded environments are very important to controlling the epidemic. This study aims to develop a point-of-care testing (POCT) system that can detect COVID-19 by detecting volatile organic compounds (VOCs) in a patient's exhaled breath using the Gradient Boosted Trees Learner Algorithm. 294 breath samples were collected from 142 patients at Istanbul Medipol Mega Hospital between December 2020 and March 2021. 84 cases out of 142 resulted in negatives, and 58 cases resulted in positives. All these breath samples have been converted into numeric values through five air sensors. 10% of the data have been used for the validation of the model, while 75% of the test data have been used for training an AI model to predict the coronavirus presence. 25% have been used for testing. The SMOTE oversampling method was used to increase the training set size and reduce the imbalance of negative and positive classes in training and test data. Different machine learning algorithms have also been tried to develop the e-nose model. The test results have suggested that the Gradient Boosting algorithm created the best model. The Gradient Boosting model provides 95% recall when predicting COVID-19 positive patients and 96% accuracy when predicting COVID-19 negative patients. |
| Anahtar Kelimeler |
| Artificial Intelligence | Breath Data | COVID-19 | E-Nose | Epidemic Disease | Machine Learning |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 1 |
| Google Scholar | 4 |