Prediction of COVID-19 disease severity using synthetic data oversampling and machine learning methods on data at first hospitalization

Yıl 2025, , 413 - 428, 16.08.2024

Kübra Köksal , Buket Doğan , Zehra Aysun Altıkardeş

https://doi.org/10.17341/gazimmfd.1348341

Öz

Covid-19 is an infectious disease caused by the SARS-CoV-2 virus, which first appeared in Wuhan, China in December 2019 and was declared a pandemic by the World Health Organization on March 11, 2020. Since its first appearance, Covid-19 has spread rapidly around the world, causing the start of a process that will negatively affect all human life, especially the health sector. In addition to measures such as hygiene, mask, distance and vaccine in the fight against Covid-19, it is aimed to provide benefits in Covid-19 diagnosis and prediction processes by developing computer-aided systems by researchers. In this study, which was developed in this direction, it is aimed to develop and test machine learning models that help predict WHO-oriented disease severity by using the laboratory and demographic characteristics of patients infected with COVID-19 at the hospital admission stage. In the study, a domestic data set created by using the information obtained from the patients who applied to Marmara University Hospital was used. The relationship between two different endpoints, namely oxygen need and intensive care need, and the first laboratory results on the dataset was analyzed using K-Nearest Neighbor, Bagging, Random Forest and Decision Tree machine learning methods. The unbalanced class distribution in the data set was balanced using the SMOTE data multiplication algorithm, and the effect of data multiplication on classification performance was evaluated in terms of accuracy and F1-Score. On the data set without SMOTE, the oxygen requirement of the patient during the first hospitalization was estimated with 16 features with 91.67% accuracy, the oxygen requirement during hospitalization with 18 features with 91.96%, and the intensive care need at hospitalization with 12 features with an accuracy of 92.17%. After SMOTE data multiplication, an increase of 6% for Analysis-1, 23% for Analysis-2 and 21% for Analysis-3 was observed in the F1-Score values of minority classes.

Anahtar Kelimeler

Covid-19, machine learning, laboratory data, prognosis

İlk yatıştaki veriler üzerinde yapay veri çoğaltma ve makine öğrenmesi yöntemleri kullanılarak COVID-19 hastalık şiddetinin tahmini

Öz

Covid-19 ilk olarak 2019 Aralık ayında Çin’in Wuhan kentinde ortaya çıkan ve 11 Mart 2020’de Dünya Sağlık Örgütü tarafından pandemi olarak ilan edilen, SARS-CoV-2 virüsünün neden olduğu bir bulaşıcı hastalıktır. Covid-19 ilk ortaya çıktığı tarihten itibaren dünya genelinde hızla yayılarak başta sağlık sektörü olmak üzere tüm insan hayatını olumsuz yönde etkileyecek bir sürecin başlamasına neden olmuştur. Covid-19 ile mücadelede hijyen, maske, mesafe ve aşı gibi önlemlerin yanında araştırmacılar tarafından bilgisayar destekli sistemler geliştirilerek Covid-19 teşhis ve tahmin süreçlerinde fayda sağlanması hedeflenmiştir. Bu doğrultuda geliştirilen bu çalışmada COVID-19 ile enfekte olmuş hastaların hastaneye kabul aşamasında alınan laboratuvar ve demografik özellikleri kullanılarak WHO odaklı hastalık şiddetini tahmin etmeye yardımcı olan makine öğrenmesi modellerinin geliştirilmesi ve test edilmesi amaçlanmaktadır. Çalışmada Marmara Üniversitesi Hastanesine başvuran hastalardan alınan bilgiler kullanılarak oluşturulan yerli bir veri seti kullanılmıştır. Veri seti üzerinde oksijen ihtiyacı ve yoğun bakım ihtiyacı olmak üzere belirlenen iki farklı sonlanım durumu ile ilk laboratuvar sonuçları arasındaki ilişki K-En yakın komşu, Torbalama (Bagging), Rastgele Orman ve Karar Ağacı makine öğrenmesi yöntemleri kullanılarak analiz edilmiştir. Veri setindeki dengesiz sınıf dağılımı SMOTE veri çoğaltma algoritması kullanılarak dengeli bir hale getirilmiş ve veri çoğaltmanın sınıflandırma performansına etkisi doğruluk ve F1-Skor açısından değerlendirilmiştir. SMOTE uygulanmayan veri seti üzerinde hastanın ilk yatış aşamasındaki oksijen ihtiyacı (Analiz – 1) 16 özellik ile %91,67, yatış sırasındaki oksijen ihtiyacı (Analiz – 2) 18 özellik ile %91,96 ve yatış sırasındaki yoğun bakım ihtiyacı (Analiz – 3) 12 özellik ile %92,17 doğruluk değeri ile tahmin edilmiştir. SMOTE veri çoğaltma işleminden sonra azınlık sınıfların F1-Skor değerlerinde Analiz – 1 için %6’lık, Analiz – 2 için %23’lik ve Analiz – 3 %21’lik bir artış gözlenmiştir.

Anahtar Kelimeler

Covid-19, makine öğrenmesi, laboratuvar verisi, prognoz

Kaynakça

• 1. Guarner J., Three Emerging Coronaviruses in Two Decades, Am J Clin Pathol, 153 (4), 420-421, 2020.
• 2. Cheruku S.R., Barina A., Kershaw C.D., Goff K., Reisch J., Hynan L.S., Ahmed F., Armaignac D.L., Patel L. Belden K.A., Palliative care consultation and end-of-life outcomes in hospitalized COVID-19 patients, Resuscitation, 170, 230-237, 2022.
• 3. Organization P.A.H. WHO characterizes COVID-19 as a pandemic. https://www3.paho.org/hq/index.php?option=com_content&view=article&id=15756:who-characterizes-covid-19-as-a-pandemic&Itemid=1926&lang=en. Yayın tarihi 2022. Erişim tarihi Temmuz 6, 2023.
• 4. Organization W.H. WHO coronavirus (COVID-19) emergency dashboard. https://covid19.who.int. Yayın tarihi 2021. Erişim tarihi Temmuz 6, 2023.
• 5. Fontanarosa P.B. Bauchner H., COVID-19—looking beyond tomorrow for health care and society, Jama, 323 (19), 1907-1908, 2020.
• 6. Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y., Zhang L., Fan G., Xu J., Gu X., Cheng Z., Yu T., Xia J., Wei Y., Wu W., Xie X., Yin W., Li H., Liu M., Xiao Y., Gao H., Guo L., Xie J., Wang G., Jiang R., Gao Z., Jin Q., Wang J. Cao B., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, Lancet, 395 (10223), 497-506, 2020.
• 7. Wang D., Hu B., Hu C., Zhu F., Liu X., Zhang J., Wang B., Xiang H., Cheng Z., Xiong Y., Zhao Y., Li Y., Wang X. Peng Z., Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China, Jama, 323 (11), 1061-1069, 2020.
• 8. Çilgin C., Gökçen H. Gökşen Y., Sentiment analysis of public sensitivity to COVID-19 vaccines on twitter by majority voting classifier-based machine learning, Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (2), 1093-1104, 2022.
• 9. Sönmez N., Terim Cavka B., Recommendations for the transformation of patient rooms into isolated patient rooms in the process of the COVID-19 pandemic, Journal of the Faculty of Engineering and Architecture of Gazi University, 38 (1), 175-188, 2022.
• 10. Banerjee A., Ray S., Vorselaars B., Kitson J., Mamalakis M., Weeks S., Baker M. Mackenzie L.S., Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population, Int Immunopharmacol, 86, 106705, 2020.
• 11. Mondal M.R.H., Bharati S., Podder P. Podder P., Data analytics for novel coronavirus disease, Informatics in Medicine Unlocked, 20, 100374, 2020.
• 12. Akarsu E., Classification of Coronavirus Disease with Artificial Intelligence and Machine Learning, Avrupa Bilim ve Teknoloji Dergisi, (36), 6-9, 2022.
• 13. Arvind V., Kim J.S., Cho B.H., Geng E. Cho S.K., Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19, J Crit Care, 62, 25-30, 2021.
• 14. Burdick H., Lam C., Mataraso S., Siefkas A., Braden G., Dellinger R.P., McCoy A., Vincent J.L., Green-Saxena A., Barnes G., Hoffman J., Calvert J., Pellegrini E. Das R., Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial, Comput Biol Med, 124, 103949, 2020.
• 15. Di Castelnuovo A., Bonaccio M., Costanzo S., Gialluisi A., Antinori A., Berselli N., Blandi L., Bruno R., Cauda R., Guaraldi G., My I., Menicanti L., Parruti G., Patti G., Perlini S., Santilli F., Signorelli C., Stefanini G.G., Vergori A., Abdeddaim A., Ageno W., Agodi A., Agostoni P., Aiello L., Al Moghazi S., Aucella F., Barbieri G., Bartoloni A., Bologna C., Bonfanti P., Brancati S., Cacciatore F., vd., Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study, Nutr Metab Cardiovasc Dis, 30 (11), 1899-1913, 2020.
• 16. Huyut M.T., Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models, IRBM, 44 (1), 100725, 2023.
• 17. Gözde Ş., Demirel E., Selen A. Aladağ Z., Evaluation of effective risk factors in COVID-19 mortality rate with DEMATEL method, Journal of the Faculty of Engineering and Architecture of Gazi University, 36 (4), 2151-2166, 2021.
• 18. Huyut M.T. Üstündağ H., Prediction of diagnosis and prognosis of COVID-19 disease by blood gas parameters using decision trees machine learning model: a retrospective observational study, Med Gas Res, 12 (2), 60-66, 2022.
• 19. Huyut M.T. Huyut Z., Forecasting of Oxidant/Antioxidant levels of COVID-19 patients by using Expert models with biomarkers used in the Diagnosis/Prognosis of COVID-19, Int Immunopharmacol, 100, 108127, 2021.
• 20. Cabitza F., Campagner A., Ferrari D., Resta C.D., Ceriotti D., Sabetta E., Colombini A., Vecchi E.D., Banfi G., Locatelli M. Carobene A., Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests, Clinical Chemistry and Laboratory Medicine (CCLM), 59 (2), 421-431, 2021.
• 21. Marshall J.C., Murthy S., Diaz J., Adhikari N., Angus D.C., Arabi Y.M., Baillie K., Bauer M., Berry S. Blackwood B., A minimal common outcome measure set for COVID-19 clinical research, The Lancet Infectious Diseases, 20 (8), e192-e197, 2020.
• 22. Napoleon D. Pavalakodi S., A new method for dimensionality reduction using k-means clustering algorithm for high dimensional data set, International Journal of Computer Applications, 13 (7), 41-46, 2011.
• 23. Wei J., Research on data preprocessing in supermarket customers data mining, 2010 2nd International Conference on Information Engineering and Computer Science, 1-4, 2010.
• 24. Kaiser J., Dealing with Missing Values in Data, Journal of Systems Integration, 5 (1), 2014.
• 25. Grossman R.L., Kamath C., Kegelmeyer P., Kumar V. Namburu R., Data mining for scientific and engineering applications, Cilt 2, Springer Science & Business Media, 2013.
• 26. Padmaja D.L. Vishnuvardhan B., Comparative study of feature subset selection methods for dimensionality reduction on scientific data, 2016 IEEE 6th International Conference on Advanced Computing (IACC), 31-34, 2016.
• 27. Read B.J., Data mining and science? Knowledge discovery in science as opposed to business, 1999.
• 28. Pereira R.B., Plastino A., Zadrozny B. Merschmann L.H., Categorizing feature selection methods for multi-label classification, Artificial Intelligence Review, 49 (1), 57-78, 2018.
• 29. Chawla N.V., Bowyer K.W., Hall L.O. Kegelmeyer W.P., SMOTE: synthetic minority over-sampling technique, Journal of artificial intelligence research, 16, 321-357, 2002.
• 30. Aydilek İ.B., Yazılım hata tahmininde kullanılan metriklerin karar ağaçlarındaki bilgi kazançlarının incelenmesi ve iyileştirilmesi, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 24 (5), 906-914, 2018.
• 31. Bhargava N., Sharma S., Purohit R. Rathore P.S., Prediction of recurrence cancer using J48 algorithm, 2017 2nd International Conference on Communication and Electronics Systems (ICCES), 386-390, 2017.
• 32. Yadav S. Shukla S., Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification, 2016 IEEE 6th International conference on advanced computing (IACC), 78-83, 2016.
• 33. Landron C., Development of an amorphous film microanalysis method, Thin Solid Films, 84 (2), 143-144, 1981.
• 34. Sun Y., Zhang H., Zhao T., Zou Z., Shen B. Yang L., A new convolutional neural network with random forest method for hydrogen sensor fault diagnosis, IEEE Access, 8, 85421-85430, 2020.
• 35. Breiman L., Bagging predictors, Machine learning, 24 (2), 123-140, 1996.
• 36. Quinlan J.R., Simplifying decision trees, International journal of man-machine studies, 27 (3), 221-234, 1987.
• 37. Xing B., Zhang H., Zhang K., Zhang L., Wu X., Shi X., Yu S. Zhang S., Exploiting EEG signals and audiovisual feature fusion for video emotion recognition, IEEE Access, 7, 59844-59861, 2019.
• 38. Alballa N. Al-Turaiki I., Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review, Informatics in medicine unlocked, 24, 100564, 2021.
• 39. Huang I., Pranata R., Lim M.A., Oehadian A. Alisjahbana B., C-reactive protein, procalcitonin, D-dimer, and ferritin in severe coronavirus disease-2019: a meta-analysis, Therapeutic advances in respiratory disease, 14, 1753466620937175, 2020.
• 40. Li Q., Cao Y., Chen L., Wu D., Yu J., Wang H., He W., Chen L., Dong F. Chen W., Hematological features of persons with COVID-19, Leukemia, 34 (8), 2163-2172, 2020.
• 41. Domínguez-Olmedo J.L., Gragera-Martínez Á., Mata J. Pachón Álvarez V., Machine learning applied to clinical laboratory data in Spain for COVID-19 outcome prediction: model development and validation, Journal of medical Internet research, 23 (4), e26211, 2021.
• 42. Aljameel S.S., Khan I.U., Aslam N., Aljabri M. Alsulmi E.S., Machine learning-based model to predict the disease severity and outcome in COVID-19 patients, Scientific programming, 2021, 1-10, 2021.
• 43. Sowjanya A.M. Mrudula O., Effective treatment of imbalanced datasets in health care using modified SMOTE coupled with stacked deep learning algorithms, Applied Nanoscience, 13 (3), 1829-1840, 2023.