COVID-19 Diagnosis from Blood Gas Using Multivariate Linear Regression

Year 2024, Volume: 11 Issue: 1, 15 - 23, 31.03.2024

Faruk Ayata , Ebubekir Seyyarer

https://doi.org/10.17350/HJSE19030000327

Cited By: 1

Abstract

With the impact of the COVID-19 outbreak, almost all scientists and nations began to show great interest in the subject for a long time. Studies in the field of outbreak, diagnosis and prevention are still ongoing. Issues such as methods developed to understand the spread mechanisms of the disease, prevention measures, vaccine and drug research are among the top priorities of the world agenda. The accuracy of the tests applied in the outbreak management has become extremely critical. In this study, it is aimed to obtain a function that finds the positive or negative COVID-19 test from the blood gas values of individuals by using Machine Learning methods to contribute to the outbreak management. Using the Multivariate Linear Regression (MLR) model, a linear function is obtained to represent the COVID-19 dataset taken from the Van province of Turkey. The data set obtained from Van Yüzüncü Yıl University Dursun Odabaş Medical Center consists of blood gas analysis samples (109 positive, 1146 negative) taken from individuals. It is thought that the linear function to be obtained by using these data will be an important method in determining the test results of individuals. Gradient Descent optimization methods are used to find the optimum values of the coefficients in the function to be obtained. In the study, the RMSProp optimization algorithm has a success rate of 58-91.23% in all measurement methods, and it is seen that it is much more successful than other optimization algorithms.

Keywords

Multivariate linear regression, COVID-19, Blood gases

Ethical Statement

The authors of this study declare that they have received an ethical permission from the Van Yüzüncü Yıl University Dursun Odabaşı Medical Center dated 20.05.2021 and numbered 52545.

Supporting Institution

Van Yüzüncü Yıl University Scientific Research Projects Coordination Unit

Project Number

FYD-2022-10096

References

• Raji P, Lakshmi G. R. D. Covid-19 pandemic analysis using regression. medRxiv. 2020. doi: 10.1101/2020.10.08.20208991.
• Bandil S, Rathore S. Study of Haematological Parameters in Malaria. Asian J. Med. Res. 2019; volume(8): PT08-PT12. doi: 10.21276/ajmr.2019.8.3.pt3.
• Demircan S. Öksürük Sesi Kayıtlarından Spektral Özellikler ile Otomatik COVID-19 Tespiti. Avrupa Bilim ve Teknoloji Dergisi. 2022. Ejosat Special Issue 2022 (ICAENS-1): 492-495. DOI: 10.31590/ejosat.1083052
• Demir F. B. And Yılmaz, E. X-Ray Görüntülerinden COVID-19 Tespiti için Derin Öğrenme Temelli Bir Yaklaşım. Avrupa Bilim ve Teknoloji Dergisi. 2021. Ejosat Special Issue 2021 (RDCONF): 627-632. DOI: 10.31590/ejosat.1039522
• Taş F, Özüdoğru O, & Bolatlı G. Bilgisayarlı Tomografi Bulguları Negatif Olan Covid-19 Hastalarının; Epidemiyolojik, Klinik ve Laboratuvar Sonuçları Açısından Değerlendirilmesi. Selçuk Sağlık Dergisi. 2021. Covid-19 Özel:18-32. Retrieved from https://dergipark.org.tr/en/pub/ssd/issue/57170/757740
• Şahin Gökçe H, Özensoy Güler Ö, Şimşek E, Karagülleoğlu Z, and Çarhan A. COVID-19 Hastalarında Yeni Bir Yaklaşım Olarak Oksihemoglobin Karboksihemoglobin, Kan Gazı Değerlerinin İncelenmesi. Longitudinal Bir Çalışma. Türk Yoğun Bakım Hemşireleri Derneği Yayın Organı. 2022; 26 (3): 92-99.
• Dokeroglu T, Sevinc E, Kucukyilmaz T, and Cosar A. A survey on new generation metaheuristic algorithms. Comput. Ind. Eng., 2019. doi: 10.1016/j.cie.2019.106040.
• Villarrubia G, De Paz J. F, Chamoso P, and De la Prieta F. Artificial neural networks used in optimization problems. Neurocomputing. 2018. doi: 10.1016/j.neucom.2017.04.075.
• Pandey M, Zakwan M, Sharma P. K, and Ahmad Z. Multiple linear regression and genetic algorithm approaches to predict temporal scour depth near circular pier in non-cohesive sediment. ISH J. Hydraul. Eng. 2020. doi: 10.1080/09715010.2018.1457455.
• Shihabudheen K. V, Mahesh M, and Pillai G. N. Particle swarm optimization based extreme learning neuro-fuzzy system for regression and classification. Expert Syst. Appl. 2018. doi: 10.1016/j.eswa.2017.09.037.
• Zhijian L, Guangyao F, Dekang S, Di W, Jiacheng G, Shicong Z, Xinyan Y, Xianping L, Lei A. A novel distributed energy system combining hybrid energy storage and a multi-objective optimization method for nearly zero- energy communities and buildings. Energy. 2022; Volume (239). ISSN 0360-5442.
• On O, AE-S. E, Mohamed T, ve Abualigah L. Ebola Optimizasyon Arama Algoritması: Doğadan Esinlenen Yeni Bir Metasezgisel Optimizasyon Algoritması. IEEE Access.2022; Volume (10): 16150-16177. doi: 10.1109/ACCESS.2022.3147821.
• Ponce-Ortega J.M, Hernández-Pérez L.G. Optimization of Process Flowsheets through Metaheuristic Techniques. Springer, UK. 2019.
• Güler E, & Yerel Kandemir S. Lineer ve Kübik Regresyon Analizleri Kullanılarak OECD Ülkelerinin CO2 Emisyonlarının Tahminlemesi. Avrupa Bilim ve Teknoloji Dergisi. Ejosat Special Issue 2022 (ICAENS-1). 2022; 175- 180. DOI: 10.31590/ejosat.1079187
• Özen N. S, Saraç S, & Koyuncu M. COVID-19 Vakalarının Makine Öğrenmesi Algoritmaları ile Tahmini: Amerika Birleşik Devletleri Örneği. Avrupa Bilim ve Teknoloji Dergisi. Ejosat. 2021; 134-139. DOI: 10.31590/ejosat.855113
• Saadatmand S, Salimifar K, Mohammadi R. Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning. Med Biol Eng Comput. 2022; 60, 957–968. https://doi.org/10.1007/s11517-022- 02519-x
• Mohan S, A J, Abugabah A, M A, Kumar Singh S, Kashif Bashir A, Sanzogni L. An approach to forecast impact of Covid-19 using supervised machine learning model. Softw Pract Exp. 2021 Apr 1:10.1002/spe.2969. doi: 10.1002/spe.2969.
• Pinter G, Felde I, Mosavi A, Ghamisi P, & Gloaguen R. COVID-19 pandemic prediction for hungary; a hybrid machine learning approach. Mathematics. 2020; Volume 8(6): 890. https://doi.org/10.3390/math8060890
• Elaziz M. A, Hosny K. M, Salah A, Darwish M. M, Lu S, & Sahlol A. T. New machine learning method for image- based diagnosis of COVID-19. PLoS One. 2020; Volume 15(6). https://doi.org/10.1371/journal.pone.0235187
• Haji S. H, & Abdulazeez A. M. Comparison of optimization techniques based on gradient descent algorithm: A review. PalArch's Journal of Archaeology of Egypt/Egyptology. 2021; Volume 18(4): 2715-2743.
• Iqbal I, Odesanmi G. A, Wang J, & Liu L. Comparative Investigation of Learning Algorithms for Image Classification with Small Dataset. Applied Artificial Intelligence. 2021; Volume 35(10): 697-716. https://doi.org/10.1080/08839514.2021.1922841
• Song C. Y, Pons A, & Yen K. AG-SGD: Angle-Based Stochastic Gradient Descent. Ieee Access. 2021; Volume ( 9): 23007-23024. https://doi.org/10.1109/Access.2021.3055993.
• Truong T. T, & Nguyen H. T. Backtracking Gradient Descent Method and Some Applications in Large Scale Optimisation. Part 2: Algorithms and Experiments. Applied Mathematics and Optimization. 2021; Volume 84(3): 2557-2586. https://doi.org/10.1007/s00245-020-09718-8
• Zaman S. M, Hasan D. M, Sakline R. I, Das D, & Alam M. A. A Comparative Analysis of Optimizers in Recurrent Neural Networks for Text Classification. 2021 Ieee Asia-Pacific Conference on Computer Science and Data Engineering (Csde). 2021. https://doi.org/10.1109/Csde53843.2021.9718394
• Abdul-Adheem W. R, Ibraheem I. K, Humaidi A. J, Alkhayyat A, Maher R. A, Abdulkareem A. I, & Azar A. T. Design and analysis of a novel generalized continuous tracking differentiator. Ain Shams Engineering Journal. 2021.
• Seyyarer E, Karci A, & Ates A. Effects of the stochastic and deterministic movements in the optimization processes. Journal of the Faculty of Engineering and Architecture of Gazi University. 2022; Volume37(2):949- 965. https://doi.org/10.17341/gazimmfd.887976
• Venkatesh B, & Anuradha J. A review of feature selection and its methods. Cybernetic and information Technologies. 2019; Volume 19(1): 3-26.
• Khaire U. M, & Dhanalakshmi R. Stability of feature selection algorithm: A review. Journal of King Saud University-Computer and Information Sciences. 2022; Volume 34(4): 1060-1073.
• Remeseiro B, & Bolon-Canedo V. A review of feature selection methods in medical applications. Computers in biology and medicine. 2019; Volume (112).
• Zebari R, Abdulazeez A, Zeebaree D, Zebari D, & Saeed J. A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction. Journal of Applied Science and Technology Trends. 2020; Volume 1(2): 56-70.
• Şener Y. Makine Öğrenmesinde Değişken Seçimi (Feature Selection) Yazı Serisi: Sarmal (Wrapper) Yöntemler ve Python Kodları. 2020. Erişim Adresi: https://yigitsener.medium.com