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DERİN SİNİR AĞLARI VE YENİDEN ÖRNEKLEME METOTLARI İLE RUTİN KAN TESTLERİNE DAYALI COVID-19 TESPİTİ

Year 2021, Volume: 9 Issue: 2, 522 - 534, 01.06.2021
https://doi.org/10.36306/konjes.877805

Abstract

İlk olarak Aralık 2019’da ortaya çıkan ve dünya çapında bir salgına neden olan Koronavirüs (COVID- 19) hastalığı; akut solunum sendromu SARS-CoV-2’nin neden olduğu viral bir hastalık olarak tanımlanmaktadır. COVID-19 hastalığının tespiti için güncel olan rRT-PCR testi kullanılmaktadır. Bu tes- tin uzun geri dönüş süresi, %15-20 civarında yanlış negatif oranları ve pahalı ekipmanları olması nedeni- yle rutin kan incelemelerinin değerleri ile tespit yöntemi daha hızlı ve daha ucuz bir alternatif olarak değerlendirilebilmektedir. Bu çalışmada, rutin kan testlerinden Derin Sinir Ağları (DSA) kullanılarak COVID-19 tespit edilmeye çalışılmıştır. Kullanılan veri setinde sınıf dengesizliği olduğu için yeniden örnekleme yöntemleriyle sınıf dengesizliği giderilmiş ve kullanılan algoritmaların performansları değer- lendirilmiştir. Yeniden örnekleme yapılırken SMOTE, ADASYN, Geometric SMOTE, Random Under- Sampler, Random OverSampler algoritmaları kullanılmıştır. Kurulan model sonunda 0,985 doğruluk değeri ve 0,99 F1-skoru ile en başarılı sonuç, Random OverSampler algoritması ile alınmıştır. Ayrıca yeni girilecek veriler için tahmin yapabilmek amacıyla, PyQt kullanılarak bir uygulama geliştirilmiştir ve kullanılan niteliklerin modele katkıları SHapley Additive Explanations (SHAP) tekniği ile belirlenmiş ve açıklanmıştır.

References

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  • Ankara, N., Sahi̇nturk, H., 2019, "Dengesiz Kredi Skorlama Veri Setlerinde Kolektif Öğrenme Algoritmalarının Performans Değerlendirmesi", PressAcademia Procedia, Vol. 9, No. 1, pp 180-185.
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  • Banerjee, A., Ray, S., Vorselaars, B., Kitson, J., Mamalakis, M., Weeks, S., Baker, M., Mackenzie, L. S., 2020, "Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population", International Immunopharmacology, Vol. 86, pp 106705.
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  • Gao, Y., Li, T., Han, M., Li, X., Wu, D., Xu, Y., Zhu, Y., Liu, Y., Wang, X., Wang, L., 2020, "Diagnostic utility of clinical laboratory data determinations for patients with the severe COVID-19", Journal of medical virology, Vol. 92, No. 7, pp 791-796.
  • Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., Bing, G., 2017, "Learning from class- imbalanced data: Review of methods and applications", Expert Systems with Applications, Vol. 73, pp 220-239.
  • Hammoudi, K., Benhabiles, H., Melkemi, M., Dornaika, F., Arganda-Carreras, I., Collard, D., Scherpereel, A., 2020, "Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19", arXiv preprint arXiv:2004.03399.
  • Jacobi, A., Chung, M., Bernheim, A., Eber, C., 2020, "Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review", Clinical Imaging, Vol. 64, pp 35-42.
  • Kaggle, Einstein Data4u, 2020. https://www.kaggle.com/einsteindata4u/covid19, Ziyaret Tarihi: 20 Aralık 2020.
  • Kamal, K. C., Yin, Z., Wu, M., Wu, Z., 2021, "Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images", Signal, Image and Video Processing, pp 1-8.
  • Kolosnjaji, B., Zarras, A., Webster, G., Eckert, C., 2016, "Deep learning for classification of malware system call sequences", Australasian Joint Conference on Artificial Intelligence, Cham, ss: 137–149, 2016.
  • Loey, M., Smarandache, F., Khalifa, N. E. M., 2020, "Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning", Symmetry, Vol. 12, No. 4, pp 651.
  • Lundberg, S., Lee, S.-I., 2017, "A unified approach to interpreting model predictions", arXiv preprint arXiv:1705.07874.
  • Maghdid, H. S., Asaad, A. T., Ghafoor, K. Z., Sadiq, A. S., Khan, M. K., 2020, "Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms", arXiv preprint arXiv:2004.00038.
  • Mezgec, S., Eftimov, T., Bucher, T., Seljak, B. K., 2019, "Mixed deep learning and natural language processing method for fake-food image recognition and standardization to help automated dietary assessment", Public health nutrition, Vol. 22, No. 7, pp 1193-1202.
  • Mohammad, Tayarani, 2020, "Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review", Chaos, Solitons & Fractals, Vol. 142, pp 110338.
  • Rajaraman, S., Siegelman, J., Alderson, P. O., Folio, L. S., Folio, L. R., Antani, S. K., 2020, "Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays", arXiv preprint arXiv:2004.08379.
  • Rodríguez-Pérez, R., Bajorath, J., 2020, "Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions", Journal of computer-aided molecular design, Vol. 34, No. 10, pp 1013-1026.
  • Schwab, P., DuMont Schütte, A., Dietz, B., Bauer, S., 2020, "Clinical Predictive Models for COVID-19: Systematic Study", Journal of Medical Internet Research, Vol. 22, No. 10, pp e21439.
  • Shilbayeh, S. A., Abonamah, A., Masri, A. A., 2020, "Partially versus Purely Data-Driven Approaches in SARS-CoV-2 Prediction", Applied Sciences, Vol. 10, No. 16, pp 5696.
  • Shoeibi, A., Khodatars, M., Alizadehsani, R., Ghassemi, N., Jafari, M., Moridian, P., Khadem, A., Sadeghi, D., Hussain, S., Zare, A., Sani, Z. A., Bazeli, J., Khozeimeh, F., Khosravi, A., Nahavandi, S., Acharya, U. R., Shi, P., 2020, "Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review", ArXiv:2007.10785.
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  • Singh, D., Kumar, V., Yadav, V., Kaur, M., 2020, "Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images", International Journal of Pattern Recognition and Artificial Intelligence, pp 2151004.
  • Soares, F., 2020, "A novel specific artificial intelligence-based method to identify COVID-19 cases using simple blood exams", MedRxiv.
  • Syeda, H. B., Syed, M., Sexton, K. W., Syed, S., Begum, S., Syed, F., Prior, F., Yu Jr, F., 2021, "Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review", JMIR medical informatics, Vol. 9, No. 1, pp e23811. T.C. Sağlık Bakanlığı, 2020. https://covid19.saglik.gov.tr/TR-66300/covid-19-nedir-.html, Ziyaret Tarihi: 31 Aralık 2020.
  • Tokmak, M., Küçüksi̇lle, E. U., 2019, "Kötü Amaçlı Windows Çalıştırılabilir Dosyalarının Derin Öğrenme İle Tespiti", Bilge International Journal of Science and Technology Research, Vol. 3, No. 1, pp 67-76.
  • Vogels, C. B., Brito, A. F., Wyllie, A. L., Fauver, J. R., Ott, I. M., Kalinich, C. C., Petrone, M. E., Casanovas- Massana, A., Muenker, M. C., Moore, A. J., 2020, "Analytical sensitivity and efficiency comparisons of SARS-CoV-2 RT–qPCR primer–probe sets", Nature microbiology, Vol. 5, No. 10, pp 1299-1305.
  • Yavaş, M., Güran, A., Uysal, M., 2020, "Covid-19 Veri Kümesinin SMOTE Tabanlı Örnekleme Yöntemi Uygulanarak Sınıflandırılması", European Journal of Science and Technology, No. Özel Sayı, pp 258- 264.
  • Zeyer, A., Doetsch, P., Voigtlaender, P., Schlüter, R., Ney, H., 2017, "A comprehensive study of deep bidirectional LSTM RNNs for acoustic modeling in speech recognition", 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ss. 2462-2466,IEEE.
  • Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., Wang, X., 2020, "Deep learning-based detection for COVID-19 from chest CT using weak label", medRxiv.

Covid-19 Detection Based on Routine Blood Tests with Deep Neural Networks and Resampling Methods

Year 2021, Volume: 9 Issue: 2, 522 - 534, 01.06.2021
https://doi.org/10.36306/konjes.877805

Abstract

Coronavirus (COVID-19) disease, which first appeared in December 2019 and caused a worldwide outbreak; is described as a viral disease caused by acute respiratory syndrome SARS-CoV-2.
The current RRT-PCR test is used to detect COVID-19 disease. Due to long return time of this test, about 15-20% false-negative rates and expensive equipment, the detection method with the values of routine blood analyses can be considered as a faster and cheaper alternative. In this study, COVID-19 was tried to be detected by using Deep Neural Networks (DNN), one of the routine blood tests. Because there is class imbalance in the used data set, class imbalance has been eliminated by resampling methods and the performance of used algorithms has been evaluated. While resampling, SMOTE, ADASYN, Geometric SMOTE, Random UnderSampler, Random OverSampler algorithms were used. As a result of established model, the most successful result was obtained with the Random OverSampler algorithm, with an accuracy of 0.985 and an F1-score of 0.99. In addition, an application has been developed using PyQt to make predictions for new data to be entered and the contributions of used attributes to the model were determined and explained with the SHapley Additive Explanations (SHAP) technique.

References

  • Ahsan, M. M., Gupta, K. D., Islam, M. M., Sen, S., Rahman, M., Hossain, M. S., 2020, "Study of different deep learning approach with explainable ai for screening patients with COVID-19 symptoms: Using ct scan and chest x-ray image dataset", arXiv preprint arXiv:2007.12525.
  • AlJame, M., Ahmad, I., Imtiaz, A., Mohammed, A., 2020, "Ensemble learning model for diagnosing COVID-19 from routine blood tests", Informatics in Medicine Unlocked, Vol. 21, pp 100449.
  • Ankara, N., Sahi̇nturk, H., 2019, "Dengesiz Kredi Skorlama Veri Setlerinde Kolektif Öğrenme Algoritmalarının Performans Değerlendirmesi", PressAcademia Procedia, Vol. 9, No. 1, pp 180-185.
  • Avila, E., Dorn, M., Alho, C. S., Kahmann, A., 2020, "Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios", ArXiv:2005.10227.
  • Banerjee, A., Ray, S., Vorselaars, B., Kitson, J., Mamalakis, M., Weeks, S., Baker, M., Mackenzie, L. S., 2020, "Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population", International Immunopharmacology, Vol. 86, pp 106705.
  • Barros, P., Parisi, G. I., Weber, C., Wermter, S., 2017, "Emotion-modulated attention improves expression recognition: A deep learning model", Neurocomputing, Vol. 253, pp 104-114.
  • Bogu, G. K., Snyder, M. P., 2021, "Deep learning-based detection of COVID-19 using wearables data", MedRxiv, pp 2021.01.08.21249474.
  • Cabitza, F., Campagner, A., Ferrari, D., Di Resta, C., Ceriotti, D., Sabetta, E., Colombini, A., De Vecchi, E., Banfi, G., Locatelli, M., Carobene, A., 2021, "Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests", Clinical Chemistry and Laboratory Medicine (CCLM), Vol. 59, No. 2, pp 421-431.
  • Cascella, M., Rajnik, M., Cuomo, A., Dulebohn, S. C., Di Napoli, R., 2020, "Features, Evaluation, and Treatment of Coronavirus", StatPearls, Treasure Island (FL): StatPearls Publishing.
  • Chassagnon, G., Vakalopoulou, M., Paragios, N., Revel, M.-P., 2020, "Artificial intelligence applications for thoracic imaging", European journal of radiology, Vol. 123, pp 108774.
  • Civit-Masot, J., Luna-Perejón, F., Domínguez Morales, M., Civit, A., 2020, "Deep learning system for COVID-19 diagnosis aid using X-ray pulmonary images", Applied Sciences, Vol. 10, No. 13, pp 4640.
  • Cui, Z., Xue, F., Cai, X., Cao, Y., Wang, G., Chen, J., 2018, "Detection of malicious code variants based on deep learning", IEEE Transactions on Industrial Informatics, Vol. 14, No. 7, pp 3187-3196.
  • Czako Z., Sebestyen G., Hangan A., 2020, "Potenciális COVID-19 fertőzés automatikus felismerésé hagyományos véranalízis alapján", XXI. Energetika-Elektrotechnika – ENELKO és XXX. Számítástechnika és Oktatás – SzámOkt Multi-konferencia, pp 57–62.
  • de Freitas Barbosa, V. A., Gomes, J. C., de Santana, M. A., Albuquerque, J. E. de A., de Souza, R. G., de Souza, R. E., dos Santos, W. P., 2021, "Heg.IA: an intelligent system to support diagnosis of Covid-19 based on blood tests", Research on Biomedical Engineering.
  • de Moraes Batista, A. F., Miraglia, J. L., Rizzi Donato, T. H., Porto Chiavegatto Filho, A. D., 2020, "COVID-19 diagnosis prediction in emergency care patients: a machine learning approach" (preprint),Epidemiology. https://doi.org/10.1101/2020.04.04.20052092
  • Dlotko, P., Rudkin, S., 2020, "Covid-19 clinical data analysis using Ball Mapper" (preprint),Intensive Care and Critical Care Medicine. https://doi.org/10.1101/2020.04.10.20061374
  • Fan, B. E., Chong, V. C. L., Chan, S. S. W., Lim, G. H., Lim, K. G. E., Tan, G. B., Mucheli, S. S., Kuperan, P., Ong, K. H., 2020, "Hematologic parameters in patients with COVID-19 infection", American journal of hematology, Vol. 95, No. 6, pp E131-E134.
  • Formica, V., Minieri, M., Bernardini, S., Ciotti, M., D’Agostini, C., Roselli, M., Andreoni, M., Morelli, C., Parisi, G., Federici, M., Paganelli, C., Legramante, J. M., 2020, "Complete blood count might help to identify subjects with high probability of testing positive to SARS-CoV-2", Clinical Medicine, Vol. 20, No. 4, pp e114-e119.
  • Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., Herrera, F., 2011, "A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches", IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol. 42, No. 4, pp 463-484.
  • Gao, Y., Li, T., Han, M., Li, X., Wu, D., Xu, Y., Zhu, Y., Liu, Y., Wang, X., Wang, L., 2020, "Diagnostic utility of clinical laboratory data determinations for patients with the severe COVID-19", Journal of medical virology, Vol. 92, No. 7, pp 791-796.
  • Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., Bing, G., 2017, "Learning from class- imbalanced data: Review of methods and applications", Expert Systems with Applications, Vol. 73, pp 220-239.
  • Hammoudi, K., Benhabiles, H., Melkemi, M., Dornaika, F., Arganda-Carreras, I., Collard, D., Scherpereel, A., 2020, "Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19", arXiv preprint arXiv:2004.03399.
  • Jacobi, A., Chung, M., Bernheim, A., Eber, C., 2020, "Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review", Clinical Imaging, Vol. 64, pp 35-42.
  • Kaggle, Einstein Data4u, 2020. https://www.kaggle.com/einsteindata4u/covid19, Ziyaret Tarihi: 20 Aralık 2020.
  • Kamal, K. C., Yin, Z., Wu, M., Wu, Z., 2021, "Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images", Signal, Image and Video Processing, pp 1-8.
  • Kolosnjaji, B., Zarras, A., Webster, G., Eckert, C., 2016, "Deep learning for classification of malware system call sequences", Australasian Joint Conference on Artificial Intelligence, Cham, ss: 137–149, 2016.
  • Loey, M., Smarandache, F., Khalifa, N. E. M., 2020, "Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning", Symmetry, Vol. 12, No. 4, pp 651.
  • Lundberg, S., Lee, S.-I., 2017, "A unified approach to interpreting model predictions", arXiv preprint arXiv:1705.07874.
  • Maghdid, H. S., Asaad, A. T., Ghafoor, K. Z., Sadiq, A. S., Khan, M. K., 2020, "Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms", arXiv preprint arXiv:2004.00038.
  • Mezgec, S., Eftimov, T., Bucher, T., Seljak, B. K., 2019, "Mixed deep learning and natural language processing method for fake-food image recognition and standardization to help automated dietary assessment", Public health nutrition, Vol. 22, No. 7, pp 1193-1202.
  • Mohammad, Tayarani, 2020, "Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review", Chaos, Solitons & Fractals, Vol. 142, pp 110338.
  • Rajaraman, S., Siegelman, J., Alderson, P. O., Folio, L. S., Folio, L. R., Antani, S. K., 2020, "Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays", arXiv preprint arXiv:2004.08379.
  • Rodríguez-Pérez, R., Bajorath, J., 2020, "Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions", Journal of computer-aided molecular design, Vol. 34, No. 10, pp 1013-1026.
  • Schwab, P., DuMont Schütte, A., Dietz, B., Bauer, S., 2020, "Clinical Predictive Models for COVID-19: Systematic Study", Journal of Medical Internet Research, Vol. 22, No. 10, pp e21439.
  • Shilbayeh, S. A., Abonamah, A., Masri, A. A., 2020, "Partially versus Purely Data-Driven Approaches in SARS-CoV-2 Prediction", Applied Sciences, Vol. 10, No. 16, pp 5696.
  • Shoeibi, A., Khodatars, M., Alizadehsani, R., Ghassemi, N., Jafari, M., Moridian, P., Khadem, A., Sadeghi, D., Hussain, S., Zare, A., Sani, Z. A., Bazeli, J., Khozeimeh, F., Khosravi, A., Nahavandi, S., Acharya, U. R., Shi, P., 2020, "Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review", ArXiv:2007.10785.
  • Shorten, C., Khoshgoftaar, T. M., Furht, B., 2021, "Deep Learning applications for COVID-19", Journal of Big Data, Vol. 8, No. 1, pp 1-54.
  • Singh, D., Kumar, V., Yadav, V., Kaur, M., 2020, "Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images", International Journal of Pattern Recognition and Artificial Intelligence, pp 2151004.
  • Soares, F., 2020, "A novel specific artificial intelligence-based method to identify COVID-19 cases using simple blood exams", MedRxiv.
  • Syeda, H. B., Syed, M., Sexton, K. W., Syed, S., Begum, S., Syed, F., Prior, F., Yu Jr, F., 2021, "Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review", JMIR medical informatics, Vol. 9, No. 1, pp e23811. T.C. Sağlık Bakanlığı, 2020. https://covid19.saglik.gov.tr/TR-66300/covid-19-nedir-.html, Ziyaret Tarihi: 31 Aralık 2020.
  • Tokmak, M., Küçüksi̇lle, E. U., 2019, "Kötü Amaçlı Windows Çalıştırılabilir Dosyalarının Derin Öğrenme İle Tespiti", Bilge International Journal of Science and Technology Research, Vol. 3, No. 1, pp 67-76.
  • Vogels, C. B., Brito, A. F., Wyllie, A. L., Fauver, J. R., Ott, I. M., Kalinich, C. C., Petrone, M. E., Casanovas- Massana, A., Muenker, M. C., Moore, A. J., 2020, "Analytical sensitivity and efficiency comparisons of SARS-CoV-2 RT–qPCR primer–probe sets", Nature microbiology, Vol. 5, No. 10, pp 1299-1305.
  • Yavaş, M., Güran, A., Uysal, M., 2020, "Covid-19 Veri Kümesinin SMOTE Tabanlı Örnekleme Yöntemi Uygulanarak Sınıflandırılması", European Journal of Science and Technology, No. Özel Sayı, pp 258- 264.
  • Zeyer, A., Doetsch, P., Voigtlaender, P., Schlüter, R., Ney, H., 2017, "A comprehensive study of deep bidirectional LSTM RNNs for acoustic modeling in speech recognition", 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ss. 2462-2466,IEEE.
  • Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., Wang, X., 2020, "Deep learning-based detection for COVID-19 from chest CT using weak label", medRxiv.
There are 45 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Research Article
Authors

Mahmut Tokmak 0000-0003-0632-4308

Ecir Küçüksille 0000-0002-3293-9878

Publication Date June 1, 2021
Submission Date February 9, 2021
Acceptance Date March 12, 2021
Published in Issue Year 2021 Volume: 9 Issue: 2

Cite

IEEE M. Tokmak and E. Küçüksille, “DERİN SİNİR AĞLARI VE YENİDEN ÖRNEKLEME METOTLARI İLE RUTİN KAN TESTLERİNE DAYALI COVID-19 TESPİTİ”, KONJES, vol. 9, no. 2, pp. 522–534, 2021, doi: 10.36306/konjes.877805.