Acil Bakım Hastalarında Evrişimli Sinir Ağını Kullanarak COVID-19 Hastalığının Tahmini

Koronavirüs hastalığı (COVID-19) vakalarındaki ani artış dünya genelinde birçok ülkenin sağlık hizmetleriüzerinde yüksek bir baskı oluşturmaktadır. Mevcut durumda hastalığın erken ve doğru tanısının koyuluptedaviye başlanması hayati önem taşımaktadır. COVID-19 için en doğrulanmış tanı testi olan RT-PCRgelişmekte olan ülkelerin çoğunda yetersizdir. Bu durum enfekte olan hasta sayısını arttırmakta veönleyici tedbirleri geciktirmektedir. Bu çalışma ile acil servise gelen tüm hastalardan rutin olarak alınankan testlerinden elde edilen veri kümesine derin öğrenme modellerinden Evrişimsel sinir ağı (CNN)yöntemi uygulanarak pozitif COVID-19 tanısı riski tahmin edilmektedir. Deneylerde kullanılan verikümesi Brezilya, São Paulo’de bulunan Israelita Albert Einstein hastanesine başvuran hastalardan 28Mart – 3 Nisan tarihleri arasındaki alınan verilerden oluşmaktadır. Veri kümesine J48, YSA, RandomForest ve Random Comittee veri madenciliği algoritmalarının yanında CNN derin öğrenme algoritmasıuygulanmıştır. Veri kümesine 5 ve 7 katlı çapraz geçerlilik modeli uygulanarak objektifliğin sağlanmasıaçısından iki modelin ortalaması değerlendirme ölçütü olarak kullanılmıştır. En iyi tahmin performansıolan %92.52 doğruluk değeri CNN yöntemi ile elde edilmiştir. Deneysel sonuçlar, önerilen yaklaşımıngenel geçerliliği bulunan testlerin sonuçları ile paralellik gösterdiğini ortaya koymaktadır.

COVID-19 Diagnosis Prediction in Emergency Care Patients usingConvolutional Neural Network

The sudden increase in cases of Coronavirus disease (COVID-19) puts a high pressure on health care providers in many countries across the world. In the present case, an early and correct diagnosis of the disease, and starting the treatment is of vital importance. Most of the developing countries have insufficient RT-PCR tests, the most verified diagnostic test for COVID-19. This increases the number of infected patients and delays preventive measures. In this study, the risk of a positive COVID-19 diagnosis is estimated by applying Convolutional Neural Network (CNN) method, which is a deep learning model, to the dataset obtained from routine blood tests of all patients who admitted to the emergency service. The dataset used in the experiments consists of the data from patients admitted to the Israelita Albert Einstein Hospital in São Paulo, Brazil, between March 28th and April 3rd, 2020. In addition to the J48, ANN, Random Forest, and Random Committee data mining algorithms, the CNN deep learning algorithm were applied to the dataset. The 5 and 7 fold cross validation model was applied to the data set and the average of the two models was used as an evaluation criterion in order to ensure objectivity.The best prediction performance was obtained by the CNN method by 92.52% accuracy. Experimental results revealed that the proposed approach is in line with the results of the tests with general validity.

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Adem, K., 2018. Exudate detection for diabetic retinopathy with circular Hough transformation and convolutional neural networks. Expert Systems with Applications, 114, 289-295.
Adem, K., Hekim, M., and Demir, S., 2019. Detection of hemorrhage in retinal images using linear classifiers and iterative thresholding approaches based on firefly and particle swarm optimization algorithms. Turkish Journal of Electrical Engineering & Computer Sciences, 27(1), 499-515.
Aktoz M., Altay H., Aslanger E., Atalar E., Aytekin V., Baykan A. O., Barçın C., Barış N., Boyacı A. A., 2020. COVID-19 pandemisi ve kardiyovasküler hastalıklar konusunda bilinmesi gerekenler. Turk Kardiyol Dern Ars, 48, 1-87.
Ali, J., Khan, R., Ahmad, N., & Maqsood, I., 2012. Random forests and decision trees. International Journal of Computer Science Issues (IJCSI), 9(5), 272.
Ayyoubzadeh, S. M., Ayyoubzadeh, S. M., Zahedi, H., Ahmadi, M., and Kalhori, S. R. N., 2020. Predicting COVID-19 ıncidence through analysis of google trends data in ıran: data mining and deep learning pilot study. JMIR Public Health and Surveillance, 6, e18828.
Banerjee, A., Ray, S., Vorselaars, B., Kitson, J., Mamalakis, M., Weeks, S., ... and 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, 86, 106705.
Castro, J. D. S., 2021. Discrimination of SARS-Cov 2 and arboviruses (DENV, ZIKV and CHIKV) clinical features using machine learning techniques: a fast and inexpensive clinical screening for countries simultaneously affected by both diseases. medRxiv.
Ciresan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J., 2010. Deep big simple neural nets for handwritten digit recognition. Neural Computation, 22, 3207–3220.
de Moraes Batista, A. F., Miraglia, J. L., Donato, T. H. R., & Chiavegatto Filho, A. D. P., 2020. COVID-19 diagnosis prediction in emergency care patients: a machine learning approach. medRxiv, 1-8.
Deng, L. and Yu, D., 2014. Deep learning: methods and applications. Foundations and trends in signal processing, 7,197-387.
Erkan, U. and Thanh, D. N., 2019. Autism spectrum disorder detection with machine learning methods. Current Psychiatry Research and Reviews Formerly: Current Psychiatry Reviews, 15, 297-308.
Ge, Y., Tian, T., Huang, S., Wan, F., Li, J., Li, S., ... & Cheng, L., 2020. A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19. bioRxiv, 1-62.
Kang, H. (2013). The prevention and handling of the missing data. Korean journal of anesthesiology, 64(5), 402.
LeCun, Y., Bengio, Y., & Hinton, G., 2015. Deep learning. nature, 521, 436-444.
LeCun, Y., Boser, B. E., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W. E., & Jackel, L. D., 1990. Handwritten digit recognition with a backpropagation network. In Advances in neural information processing systems, 396-404.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278-2324.
Little, R. J., D'Agostino, R., Cohen, M. L., Dickersin, K., Emerson, S. S., Farrar, J. T., ... & Stern, H., 2012. The prevention and treatment of missing data in clinical trials. New England Journal of Medicine, 367(14), 1355-1360.
Lu, R., Zhao, X., Li, J., Niu, P., Yang, B., Wu, H., ... & Bi, Y., 2020. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. The Lancet, 395, 565-574.
Metsky, H. C., Freije, C. A., Kosoko-Thoroddsen, T. S. F., Sabeti, P. C., & Myhrvold, C., 2020. CRISPR-based COVID-19 surveillance using a genomicallycomprehensive machine learning approach. bioRxiv, 1-11.
Pandey, G., Chaudhary, P., Gupta, R., & Pal, S., 2020. SEIR and Regression Model based COVID-19 outbreak predictions in India. arXiv preprint, 1-10.
Randhawa, G. S., Soltysiak, M. P., El Roz, H., de Souza, C. P., Hill, K. A., & Kari, L., 2020. Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. Plos one, 15(4), e0232391.
Sanderson, M., Bulloch, A. G., Wang, J., Williamson, T., & Patten, S. B., 2020. Predicting death by suicide using administrative health care system data: Can recurrent neural network, one-dimensional convolutional neural network, and gradient boosted trees models improve prediction performance?. Journal of affective disorders, 264, 107-114.
Santoso, P. H., Fauziah, F., & Nurhayati, N., 2020. Application of data mining classification for covid19 ınfected status using algortima naïve method. Jurnal Mantik, 4, 267-275.
Schwab, P., Schütte, A. D., Dietz, B., & Bauer, S. (2020). Clinical predictive models for COVID-19: systematic study. Journal of medical Internet research, 22(10), e21439.
Shaker, A. M., Tantawi, M., Shedeed, H. A., & Tolba, M. F., 2019. Heartbeat classification using 1D convolutional neural networks. In International Conference on Advanced Intelligent Systems and Informatics (pp. 502-511).
Simard PY, Steinkraus D, Platt JC., 2003. Best practices for convolutional neural networks applied to visual document analysis. In Proceedings of the Seventh International Conference on Document Analysis and Recognition, 2: 958–962.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R., 2014. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15, 1929- 1958.
Tanaydin, A., Liang, J., & Engels, D. W., 2020. SARS-CoV2 pandemic analytical overview with machine learning Predictability. SMU Data Science Review, 3(2), 17.
Virgeniya, S. C., & Ramaraj, E., 2020. Predictive Modeling Algorithms-based Classification of Arrhythmia. In 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 1272-1276).
Wu, Z. and McGoogan, J. M., 2020. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the chinese center for disease control and prevention. Jama, 323, 1239- 1242.
Yan, L., Zhang, H. T., Xiao, Y., Wang, M., Sun, C., Liang, J., ... & Tang, X., 2020. Prediction of survival for severe Covid-19 patients with three clinical features: development of a machine learning-based prognostic model with clinical data in Wuhan. medRxiv, 1-14.
Yao, H., Zhang, N., Zhang, R., Duan, M., Xie, T., Pan, J., ... & Wang, G., 2020. Severity detection for the coronavirus disease 2019 (covid-19) patients using a machine learning model based on the blood and urine tests. Frontiers in cell and developmental biology, 8, 683.
Yavaş, M., Güran, A., & Uysal, M. Covid-19 Veri Kümesinin SMOTE Tabanlı Örnekleme Yöntemi Uygulanarak Sınıflandırılması. Avrupa Bilim ve Teknoloji Dergisi, (Special Issue), 258-264.
Zhou, P., Yang, X. L., Wang, X. G., Hu, B., Zhang, L., Zhang, W., ... & Chen, H. D., 2020. A pneumonia outbreak associated with a new coronavirus of probable bat origin. nature, 579, 270-273.