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Year 2022, Volume: 5 Issue: 1, 71 - 83, 30.04.2022

Hilal Arslan Orhan Er

https://doi.org/10.35377/saucis...932400

Abstract

References

  • [1] Yazeed Zoabi, Shira Deri-Rozov, and Noam Shomron. Machine learning-based prediction of COVID-19 diagnosis based on symptoms. Digital Medicine, 4(1):3, December 2021.
  • [2] Hilal Arslan and Hasan Arslan. A new covid-19 detection method from human genome sequences using cpg island features and knn classifier. Engineering Science and Technology, an International Journal, 2021.
  • [3] Hilal Arslan. Machine learning methods for covid-19 prediction using human genomic data. Proceedings, 74(1), 2021.
  • [4] Weifeng Shang, Junwu Dong, Yali Ren, Ming Tian, Wei Li, Jianwu Hu, and Yuanyuan Li. The value of clinical parameters in predicting the severity of COVID-19. Journal of Medical Virology, 92(10):2188{2192, June 2020.
  • [5] Talha Burak Alakus and Ibrahim Turkoglu. Comparison of deep learning approaches to predict covid-19 infection. Chaos, Solitons Fractals, 140:110120, 2020.
  • [6] Moutaz Alazab, Albara Awajan, Abdelwadood Mesleh, Ajith Abraham, Vansh Jatana, and Salah Alhyari4. Covid-19 prediction and detection using deep learning. International Journal of Computer Information Systems and Industrial Management Applications, 12:168-181, 2020.
  • [7] Vardan Andriasyan, Artur Yakimovich, Fanny Georgi, Anthony Petkidis, Robert Witte, Daniel Puntener, and Urs F. Greber. Deep learning of virus infections reveals mechanics of lytic cells. October 2019.
  • [8] Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Zidek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, and Demis Hassabis. Improved protein structure prediction using potentials from deep learning. Nature, 577(7792):706-710, January 2020.
  • [9] Yazeed Zoabi, Shira Deri-Rozov, and Noam Shomron. Machine learning-based prediction of COVID-19 diagnosis based on symptoms. npj Digital Medicine, 4(1):3, December 2021.
  • [10] L. J. Muhammad, Ebrahem A. Algehyne, Sani Sharif Usman, Abdulkadir Ahmad, Chinmay Chakraborty, and I. A. Mohammed. Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset. SN Computer Science, 2(1):11, February 2021.
  • [11] Sina F. Ardabili, Amir Mosavi, Pedram Ghamisi, Filip Ferdinand, Annamaria R. Varkonyi-Koczy, Uwe Reuter, Timon Rabczuk, and Peter M. Atkinson. COVID-19 Outbreak Prediction with Machine Learning. Algorithms, 13(10):249, October 2020.
  • [12] Mohammad-H. Tayarani N. Applications of arti_cial intelligence in battling against covid-19: A literature review. Chaos, Solitons & Fractals, 142:110338, January 2021.
  • [13] Shashi Kushwaha, Shashi Bahl, Ashok Kumar Bagha, Kulwinder Singh Parmar, Mohd Javaid, Abid Haleem, and Ravi Pratap Singh. Significant applications of machine learning for covid-19 pandemic. Journal of Industrial Integration and Management, 5(4), December 2020.
  • [14] Francesca De Felice and Antonella Polimeni. Coronavirus Disease (COVID-19): A Machine Learning Bibliometric Analysis. In Vivo, 34(3 suppl):1613-1617, 2020.
  • [15] Federico Cabitza, Andrea Campagner, Davide Ferrari, Chiara Di Resta, Daniele Ceriotti, Eleonora Sabetta, Alessandra Colombini, Elena De Vecchi, Giuseppe Banfl, Massimo Locatelli, and Anna Carobene. 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.
  • [16] Yavuz Unal and Muhammed Nuri Dudak. Classification of covid-19 dataset with some machine learning methods. Journal of Amasya University the Institute of Sciences and Technology, 1:30 - 37, 2020.
  • [17] Xiangao Jiang, Megan Coffee, Anasse Bari, Junzhang Wang, Xinyue Jiang, Jianping Huang, Jichan Shi, Jianyi Dai, Jing Cai, Tianxiao Zhang, Zhengxing Wu, Guiqing He, and Yitong Huang. Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. Computers, Materials & Continua, 62(3):537-551, 2020.
  • [18] Andre Filipe de Moraes Batista, Jo~ao Luiz Miraglia, Thiago Henrique Rizzi Donato, and Alexandre Dias Porto Chiavegatto Filho. COVID-19 diagnosis prediction in emergency care patients: a machine learning approach. April 2020.
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  • [20] 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), 59(2), 421-431. https://doi.org/10.1515/cclm-2020-1294
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  • [22] Er O, Tanrikulu AC, Abakay A & Temurtas F. An approach based on probabilistic neural network for diagnosis of Mesothelioma’s disease. Computers & Electrical Engineering, 38(1), 75-81, 2012.
  • [23] Lu LE, Zheng Y, Carneiro G, Yang L. Deep learning and convolutional neural networks for medical image computing: Advances in Computer Vision and Pattern Recognition, Springer, 2017.
  • [24] Aghdam HA, Heravi EJ, Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification, Springer, 1st edn., 2017.
  • [25] Olmez, E., Akdogan, V., Korkmaz, M., & Er, O. (2020). Automatic Segmentation of Meniscus in Multispectral MRI Using Regions with Convolutional Neural Network (R-CNN). Journal of Digital Imaging, 33, 916-929.
  • [26] Olmez E. Automatic segmentation of meniscus in MRI using deep learning and morphological image processing, Mechatronics Engineering, PhD Thesis, Yozgat Bozok University, 2020.
  • [27] Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., & Hullender, G. (2005, August). Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning (pp. 89-96).

A Comparative Study on COVID-19 Prediction Using Deep Learning and Machine Learning Algorithms: A Case Study on Performance Analysis

Year 2022, Volume: 5 Issue: 1, 71 - 83, 30.04.2022

Hilal Arslan Orhan Er

https://doi.org/10.35377/saucis...932400

Abstract

COVID-19 disease has been the most important disease recently and has affected serious number of people in the world. There is not proven treatment method yet and early diagnosis of COVID-19 is crucial to prevent spread of the disease. Laboratory data can be easily accessed in about 15 minutes, and cheaper than the cost of other COVID-19 detection methods such as CT imaging and RT-PCR test. In this study, we perform a comparative study for COVID-19 prediction using machine learning and deep learning algorithms from laboratory findings. For this purpose, nine different machine learning algorithms including different structures as well as deep neural network classifier are evaluated and compared. Experimental results conduct that cosine k-nearest neighbor classifier achieves better accuracy with 89% among other machine learning algorithms. Furthermore, deep neural network classifier achieves an accuracy of 90.3% when one hidden layer including 60 neurons is used to detect COVID-19 disease from laboratory findings data.

Keywords

COVID-19 disease SARS-CoV-2 laboratory data machine learning deep learning

References

  • [1] Yazeed Zoabi, Shira Deri-Rozov, and Noam Shomron. Machine learning-based prediction of COVID-19 diagnosis based on symptoms. Digital Medicine, 4(1):3, December 2021.
  • [2] Hilal Arslan and Hasan Arslan. A new covid-19 detection method from human genome sequences using cpg island features and knn classifier. Engineering Science and Technology, an International Journal, 2021.
  • [3] Hilal Arslan. Machine learning methods for covid-19 prediction using human genomic data. Proceedings, 74(1), 2021.
  • [4] Weifeng Shang, Junwu Dong, Yali Ren, Ming Tian, Wei Li, Jianwu Hu, and Yuanyuan Li. The value of clinical parameters in predicting the severity of COVID-19. Journal of Medical Virology, 92(10):2188{2192, June 2020.
  • [5] Talha Burak Alakus and Ibrahim Turkoglu. Comparison of deep learning approaches to predict covid-19 infection. Chaos, Solitons Fractals, 140:110120, 2020.
  • [6] Moutaz Alazab, Albara Awajan, Abdelwadood Mesleh, Ajith Abraham, Vansh Jatana, and Salah Alhyari4. Covid-19 prediction and detection using deep learning. International Journal of Computer Information Systems and Industrial Management Applications, 12:168-181, 2020.
  • [7] Vardan Andriasyan, Artur Yakimovich, Fanny Georgi, Anthony Petkidis, Robert Witte, Daniel Puntener, and Urs F. Greber. Deep learning of virus infections reveals mechanics of lytic cells. October 2019.
  • [8] Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Zidek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, and Demis Hassabis. Improved protein structure prediction using potentials from deep learning. Nature, 577(7792):706-710, January 2020.
  • [9] Yazeed Zoabi, Shira Deri-Rozov, and Noam Shomron. Machine learning-based prediction of COVID-19 diagnosis based on symptoms. npj Digital Medicine, 4(1):3, December 2021.
  • [10] L. J. Muhammad, Ebrahem A. Algehyne, Sani Sharif Usman, Abdulkadir Ahmad, Chinmay Chakraborty, and I. A. Mohammed. Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset. SN Computer Science, 2(1):11, February 2021.
  • [11] Sina F. Ardabili, Amir Mosavi, Pedram Ghamisi, Filip Ferdinand, Annamaria R. Varkonyi-Koczy, Uwe Reuter, Timon Rabczuk, and Peter M. Atkinson. COVID-19 Outbreak Prediction with Machine Learning. Algorithms, 13(10):249, October 2020.
  • [12] Mohammad-H. Tayarani N. Applications of arti_cial intelligence in battling against covid-19: A literature review. Chaos, Solitons & Fractals, 142:110338, January 2021.
  • [13] Shashi Kushwaha, Shashi Bahl, Ashok Kumar Bagha, Kulwinder Singh Parmar, Mohd Javaid, Abid Haleem, and Ravi Pratap Singh. Significant applications of machine learning for covid-19 pandemic. Journal of Industrial Integration and Management, 5(4), December 2020.
  • [14] Francesca De Felice and Antonella Polimeni. Coronavirus Disease (COVID-19): A Machine Learning Bibliometric Analysis. In Vivo, 34(3 suppl):1613-1617, 2020.
  • [15] Federico Cabitza, Andrea Campagner, Davide Ferrari, Chiara Di Resta, Daniele Ceriotti, Eleonora Sabetta, Alessandra Colombini, Elena De Vecchi, Giuseppe Banfl, Massimo Locatelli, and Anna Carobene. 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.
  • [16] Yavuz Unal and Muhammed Nuri Dudak. Classification of covid-19 dataset with some machine learning methods. Journal of Amasya University the Institute of Sciences and Technology, 1:30 - 37, 2020.
  • [17] Xiangao Jiang, Megan Coffee, Anasse Bari, Junzhang Wang, Xinyue Jiang, Jianping Huang, Jichan Shi, Jianyi Dai, Jing Cai, Tianxiao Zhang, Zhengxing Wu, Guiqing He, and Yitong Huang. Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. Computers, Materials & Continua, 62(3):537-551, 2020.
  • [18] Andre Filipe de Moraes Batista, Jo~ao Luiz Miraglia, Thiago Henrique Rizzi Donato, and Alexandre Dias Porto Chiavegatto Filho. COVID-19 diagnosis prediction in emergency care patients: a machine learning approach. April 2020.
  • [19] Patrick Schwab, August DuMont Schutte, Benedikt Dietz, and Stefan Bauer. Clinical predictive models for COVID-19: Systematic study. Journal of Medical Internet Research, 22(10):e21439, October 2020.
  • [20] 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), 59(2), 421-431. https://doi.org/10.1515/cclm-2020-1294
  • [21] Mathworks, “Introducing Machine Learning”, e-book about MATLAB and Simulink, the MathWorks Inc., 2016.
  • [22] Er O, Tanrikulu AC, Abakay A & Temurtas F. An approach based on probabilistic neural network for diagnosis of Mesothelioma’s disease. Computers & Electrical Engineering, 38(1), 75-81, 2012.
  • [23] Lu LE, Zheng Y, Carneiro G, Yang L. Deep learning and convolutional neural networks for medical image computing: Advances in Computer Vision and Pattern Recognition, Springer, 2017.
  • [24] Aghdam HA, Heravi EJ, Guide to Convolutional Neural Networks: A Practical Application to Traffic-Sign Detection and Classification, Springer, 1st edn., 2017.
  • [25] Olmez, E., Akdogan, V., Korkmaz, M., & Er, O. (2020). Automatic Segmentation of Meniscus in Multispectral MRI Using Regions with Convolutional Neural Network (R-CNN). Journal of Digital Imaging, 33, 916-929.
  • [26] Olmez E. Automatic segmentation of meniscus in MRI using deep learning and morphological image processing, Mechatronics Engineering, PhD Thesis, Yozgat Bozok University, 2020.
  • [27] Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., & Hullender, G. (2005, August). Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning (pp. 89-96).
There are 27 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Articles
Authors

Hilal Arslan 0000-0002-6449-6952

Orhan Er 0000-0002-4732-9490

Publication Date April 30, 2022
Submission Date May 4, 2021
Acceptance Date March 29, 2022
Published in Issue Year 2022Volume: 5 Issue: 1

Cite

IEEE H. Arslan and O. Er, “A Comparative Study on COVID-19 Prediction Using Deep Learning and Machine Learning Algorithms: A Case Study on Performance Analysis”, SAUCIS, vol. 5, no. 1, pp. 71–83, 2022, doi: 10.35377/saucis...932400.
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Sakarya University Journal of Computer and Information Sciences in Applied Sciences and Engineering: An interdisciplinary journal of information science      28938