A Survey of the Application of Artifical Intellegence on COVID-19 Diagnosis and Prediction


  • H. Alalawi Computer Science Department, College of Computer and Information Systems, Umm Al-Qura University, Saudi Arabia
  • M. Alsuwat Computer Science Department, College of Computer and Information Systems, Umm Al-Qura University, Saudi Arabia
  • H. Alhakami College of Computer and Information Systems, Umm Al Qura University, Saudi Arabia
Volume: 11 | Issue: 6 | Pages: 7824-7835 | December 2021 | https://doi.org/10.48084/etasr.4503


The importance of classification algorithms has increased in recent years. Classification is a branch of supervised learning with the goal of predicting class labels categorical of new cases. Additionally, with Coronavirus (COVID-19) propagation since 2019, the world still faces a great challenge in defeating COVID-19 even with modern methods and technologies. This paper gives an overview of classification algorithms to provide the readers with an understanding of the concept of the state-of-the-art classification algorithms and their applications used in the COVID-19 diagnosis and detection. It also describes some of the research published on classification algorithms, the existing gaps in the research, and future research directions. This article encourages both academics and machine learning learners to further strengthen the basis of classification methods.


Artificial Intelligence, Machine Learning, Deep Learning, Classification Algorithms, COVID-19, Medical image Introduction


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World Health Organization, "Q&As on COVID-19 and related health topics." https://www.who.int/emergencies/diseases/novel-coronavirus-2019/question-and-answers-hub (accessed Oct. 28, 2021).

S. Parasa, N. Reddy, D. O. Faigel, A. Repici, F. Emura, and P. Sharma, "Global Impact of the COVID-19 Pandemic on Endoscopy: An International Survey of 252 Centers From 55 Countries," Gastroenterology, vol. 159, no. 4, pp. 1579-1581.e5, Oct. 2020, https://doi.org/10.1053/j.gastro.2020.06.009.

"Coronavirus (COVID-19) Testing," Our World in Data, Mar. 05, 2020. https://ourworldindata.org/coronavirus-testing (accessed Oct. 28, 2021).

C. Iwendi et al., "COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm," Frontiers in Public Health, vol. 8, 2020, Art. no. 357, https://doi.org/10.3389/fpubh.2020.00357.

A. A. Soofi and A. Awan, "Classification Techniques in Machine Learning: Applications and Issues," Journal of Basic & Applied Sciences, vol. 13, pp. 459–465, 2017.

S. Lalmuanawma, J. Hussain, and L. Chhakchhuak, "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, vol. 139, Oct. 2020, Art. no. 110059, https://doi.org/10.1016/j.chaos.2020.110059.

M. AlJame, I. Ahmad, A. Imtiaz, and A. Mohammed, "Ensemble learning model for diagnosing COVID-19 from routine blood tests," Informatics in Medicine Unlocked, vol. 21, Jan. 2020, Art. no. 100449, https://doi.org/10.1016/j.imu.2020.100449.

Y. Mohamadou, A. Halidou, and P. T. Kapen, "A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19," Applied Intelligence, vol. 50, no. 11, pp. 3913–3925, Nov. 2020, https://doi.org/10.1007/s10489-020-01770-9.

C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273–297, Sep. 1995, https://doi.org/10.1007/BF00994018.

K. Aida-zade, A. Xocayev, and S. Rustamov, "Speech recognition using Support Vector Machines," in 10th International Conference on Application of Information and Communication Technologies, Baku, Azerbaijan, Oct. 2016, pp. 1–4, https://doi.org/10.1109/ICAICT.2016.7991664.

Y. Tian, E. Li, L. Yang, and Z. Liang, "An image processing method for green apple lesion detection in natural environment based on GA-BPNN and SVM," in International Conference on Mechatronics and Automation, Changchun, China, Aug. 2018, pp. 1210–1215, https://doi.org/10.1109/ICMA.2018.8484624.

S. Dreiseitl and L. Ohno-Machado, "Logistic regression and artificial neural network classification models: a methodology review," Journal of Biomedical Informatics, vol. 35, no. 5, pp. 352–359, Oct. 2002, https://doi.org/10.1016/S1532-0464(03)00034-0.

G. Biau and E. Scornet, "A random forest guided tour," TEST, vol. 25, no. 2, pp. 197–227, Jun. 2016, https://doi.org/10.1007/s11749-016-0481-7.

M. Zakariah, "Classification of large datasets using Random Forest Algorithm in various applications: Survey," International Journal of Engineering and Innovative Technology, vol. 4, no. 3, pp. 189–198, 2014.

C. Sohrabi et al., "World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19)," International Journal of Surgery, vol. 76, pp. 71–76, Apr. 2020, https://doi.org/10.1016/j.ijsu.2020.02.034.

I. D. Apostolopoulos and T. A. Mpesiana, "Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks," Physical and Engineering Sciences in Medicine, vol. 43, no. 2, pp. 635–640, Jun. 2020, https://doi.org/10.1007/s13246-020-00865-4.

S. Kadry, V. Rajinikanth, S. Rho, N. S. M. Raja, V. S. Rao, and K. P. Thanaraj, "Development of a Machine-Learning System to Classify Lung CT Scan Images into Normal/COVID-19 Class," arXiv:2004.13122 [cs, eess, stat], Apr. 2020, Accessed: Oct. 28, 2021. [Online]. Available: http://arxiv.org/abs/2004.13122.

T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. Rajendra Acharya, "Automated detection of COVID-19 cases using deep neural networks with X-ray images," Computers in Biology and Medicine, vol. 121, Jun. 2020, Art. no. 103792, https://doi.org/10.1016/j.compbiomed.2020.103792.

A. Carfì, R. Bernabei, and F. Landi, "Persistent Symptoms in Patients After Acute COVID-19," JAMA, vol. 324, no. 6, pp. 603–605, Aug. 2020, https://doi.org/10.1001/jama.2020.12603.

M. K. Nath, A. Kanhe, and M. Mishra, "A Novel Deep Learning Approach for Classification of COVID-19 Images," in 5th International Conference on Computing Communication and Automation, Greater Noida, India, Oct. 2020, pp. 752–757, https://doi.org/10.1109/ICCCA49541.2020.9250907.

A. Abbas, M. M. Abdelsamea, and M. M. Gaber, "Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network," Applied Intelligence, vol. 51, no. 2, pp. 854–864, Feb. 2021, https://doi.org/10.1007/s10489-020-01829-7.

E. E.-D. Hemdan, M. A. Shouman, and M. E. Karar, "COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images," arXiv:2003.11055 [cs, eess], Mar. 2020, Accessed: Oct. 28, 2021. [Online]. Available: http://arxiv.org/abs/2003.11055.

A. Narin, C. Kaya, and Z. Pamuk, "Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks," Pattern Analysis and Applications, vol. 24, no. 3, pp. 1207–1220, Aug. 2021, https://doi.org/10.1007/s10044-021-00984-y.

M. Rahimzadeh and A. Attar, "A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images," Informatics in Medicine Unlocked, vol. 19, 2020, Art. no. 100360, https://doi.org/10.1016/j.imu.2020.100360.

W. H. Self, D. M. Courtney, C. D. McNaughton, R. G. Wunderink, and J. A. Kline, "High Discordance of Chest X-ray and CT for Detection of Pulmonary Opacities in ED Patients: Implications for Diagnosing Pneumonia," The American journal of emergency medicine, vol. 31, no. 2, pp. 401–405, Feb. 2013, https://doi.org/10.1016/j.ajem.2012.08.041.

G. D. Rubin et al., "The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society," Radiology, vol. 296, no. 1, pp. 172–180, Jul. 2020, https://doi.org/10.1148/radiol.2020201365.

M. Barstugan, U. Ozkaya, and S. Ozturk, "Coronavirus (COVID-19) Classification using CT Images by Machine Learning Methods," arXiv:2003.09424 [cs, eess, stat], Mar. 2020, Accessed: Oct. 28, 2021. [Online]. Available: http://arxiv.org/abs/2003.09424.

M. Alazab, A. Awajan, A. Mesleh, A. Abraham, V. Jatana, and S. Alhyari, "COVID-19 Prediction and Detection Using Deep Learning," International Journal of Computer Information Systems and Industrial Management Applications, vol. 12, pp. 168–181, 2020.

L. Wang, Z. Q. Lin, and A. Wong, "COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images," Scientific Reports, vol. 10, no. 1, Nov. 2020, Art. no. 19549, https://doi.org/10.1038/s41598-020-76550-z.

H. Kang et al., "Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning," IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp. 2606–2614, Aug. 2020, https://doi.org/10.1109/TMI.2020.2992546.

E.-S. M. El-Kenawy, A. Ibrahim, S. Mirjalili, M. M. Eid, and S. E. Hussein, "Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images," IEEE Access, vol. 8, pp. 179317–179335, 2020, https://doi.org/10.1109/ACCESS.2020.3028012.

X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, "ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases," in Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, Jul. 2017, pp. 3462–3471, https://doi.org/10.1109/CVPR.2017.369.

H. S. Maghdid, A. T. Asaad, K. Z. Ghafoor, A. S. Sadiq, and M. K. Khan, "Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithms," Mar. 2020, Accessed: Oct. 28, 2021. [Online]. Available:

J. Rasheed, A. A. Hameed, C. Djeddi, A. Jamil, and F. Al-Turjman, "A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images," Interdisciplinary Sciences: Computational Life Sciences, vol. 13, no. 1, pp. 103–117, Mar. 2021, https://doi.org/10.1007/s12539-020-00403-6.

J. Wu et al., "Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results," Apr. 2020. https://doi.org/10.1101/2020.04.02.20051136.

H. Namazi and V. V. Kulish, "Complexity-based classification of the coronavirus disease (covid-19)," Fractals, vol. 28, no. 05, Aug. 2020, Art. no. 2050114, https://doi.org/10.1142/S0218348X20501145.

P. K. Sethy, S. K. Behera, P. K. Ratha, and P. Biswas, "Detection of Coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine," International Journal of Mathematical Engineering and Management Sciences, pp. 643–651, 2020.

D. Singh, V. Kumar, Vaishali, and M. Kaur, "Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks," European Journal of Clinical Microbiology & Infectious Diseases, vol. 39, no. 7, pp. 1379–1389, Jul. 2020, https://doi.org/10.1007/s10096-020-03901-z.

U.S. Food and Drug Administration, "FDA Approves First COVID-19 Vaccine," FDA, Aug. 23, 2021. https://www.fda.gov/news-events/press-announcements/fda-approves-first-covid-19-vaccine (accessed Oct. 28, 2021).

H. Panwar, P. K. Gupta, M. K. Siddiqui, R. Morales-Menendez, P. Bhardwaj, and V. Singh, "A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images," Chaos, Solitons & Fractals, vol. 140, Nov. 2020, Art. no. 110190, https://doi.org/10.1016/j.chaos.2020.110190.

S. Asif, Y. Wenhui, H. Jin, and S. Jinhai, "Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Network," in 6th International Conference on Computer and Communications, Chengdu, China, Dec. 2020, pp. 426–433, https://doi.org/10.1109/ICCC51575.2020.9344870.

S. Minaee, R. Kafieh, M. Sonka, S. Yazdani, and G. Jamalipour Soufi, "Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning," Medical Image Analysis, vol. 65, Oct. 2020, Art. no. 101794, https://doi.org/10.1016/j.media.2020.101794.

S. Hu et al., "Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images," IEEE Access, vol. 8, pp. 118869–118883, 2020, https://doi.org/10.1109/ACCESS.2020.3005510.

L. Li et al., "Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT," Radiology, Mar. 2020, Art. no. 200905, https://doi.org/10.1148/radiol.2020200905.

O. Gozes et al., "Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis," arXiv:2003.05037 [cs, eess], Mar. 2020, Accessed: Oct. 28, 2021. [Online]. Available: http://arxiv.org/abs/2003.05037.

M. Kaur, H. K. Gianey, D. Singh, and M. Sabharwal, "Multi-objective differential evolution based random forest for e-health applications," Modern Physics Letters B, vol. 33, no. 05, Feb. 2019, Art. no. 1950022, https://doi.org/10.1142/S0217984919500222.

A. Jaiswal, N. Gianchandani, D. Singh, V. Kumar, and M. Kaur, "Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning," Journal of Biomolecular Structure and Dynamics, vol. 39, no. 15, pp. 5682–5689, Oct. 2021, https://doi.org/10.1080/07391102.2020.1788642.

S. H. Yoo et al., "Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging," Frontiers in Medicine, vol. 7, 2020, Art. no. 427, https://doi.org/10.3389/fmed.2020.00427.

S. A. Harmon et al., "Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets," Nature Communications, vol. 11, no. 1, Aug. 2020, Art. no. 4080, https://doi.org/10.1038/s41467-020-17971-2.

L. Xiao et al., "Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019," Frontiers in Bioengineering and Biotechnology, vol. 8, 2020, Art. no. 898, https://doi.org/10.3389/fbioe.2020.00898.

R. Pal, A. A. Sekh, S. Kar, and D. K. Prasad, "Neural Network Based Country Wise Risk Prediction of COVID-19," Applied Sciences, vol. 10, no. 18, Jan. 2020, Art. no. 6448, https://doi.org/10.3390/app10186448.

A. Sedik, M. Hammad, F. E. Abd El-Samie, B. B. Gupta, and A. A. Abd El-Latif, "Efficient deep learning approach for augmented detection of Coronavirus disease," Neural computing & applications, pp. 1–18, Jan. 2021, https://doi.org/10.1007/s00521-020-05410-8.

S. Wang et al., "A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)," European Radiology, vol. 31, no. 8, pp. 6096–6104, Aug. 2021, https://doi.org/10.1007/s00330-021-07715-1.

Y. Song et al., "Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images," IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021, https://doi.org/10.1109/TCBB.2021.3065361.

S. Serte and H. Demirel, "Deep learning for diagnosis of COVID-19 using 3D CT scans," Computers in Biology and Medicine, vol. 132, p. 104306, May 2021, https://doi.org/10.1016/j.compbiomed.2021.104306.

M. Kavitha, T. Jayasankar, P. M. Venkatesh, G. Mani, C. Bharatiraja, and B. Twala, "COVID-19 Disease Diagnosis using Smart Deep Learning Techniques," Journal of Applied Science and Engineering, vol. 24, no. 3, pp. 271–277, 2021, https://doi.org/10.6180/jase.202106_24(3).0001.

T. Mahmud, M. A. Rahman, and S. A. Fattah, "CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization," Computers in Biology and Medicine, vol. 122, Jul. 2020, Art. no. 103869, https://doi.org/10.1016/j.compbiomed.2020.103869.

M. Malik et al., "Determination of COVID-19 Patients Using Machine Learning Algorithms," Intelligent Automation and Soft Computing, vol. 31, no. 1, pp. 207–222, 2022.

X. Deng, H. Shao, L. Shi, X. Wang, and T. Xie, "A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models," Computer Modeling in Engineering & Sciences, vol. 125, no. 2, pp. 579–596, Nov. 2020, https://doi.org/10.32604/cmes.2020.011920.

A. Hamed, A. Sobhy, and H. Nassar, "Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm," Arabian Journal for Science and Engineering, vol. 46, no. 9, pp. 8261–8272, Sep. 2021, https://doi.org/10.1007/s13369-020-05212-z.

J. P. Cohen, P. Morrison, L. Dao, K. Roth, T. Q. Duong, and M. Ghassemi, "COVID-19 Image Data Collection: Prospective Predictions Are the Future," arXiv:2006.11988 [cs, eess, q-bio], Dec. 2020, Accessed: Oct. 28, 2021. [Online]. Available: http://arxiv.org/abs/2006.11988.

J. P. Cohen, ieee8023/covid-chestxray-dataset. 2021. Accessed: Oct. 28, 2021. [Online]. Available: https://github.com/ieee8023/covid-chestxray-dataset.

"Chest X-Ray Images (Pneumonia)." https://kaggle.com/paultimothymooney/chest-xray-pneumonia (accessed Oct. 28, 2021).

M. E. H. Chowdhury et al., "Can AI Help in Screening Viral and COVID-19 Pneumonia?," IEEE Access, vol. 8, pp. 132665–132676, 2020, https://doi.org/10.1109/ACCESS.2020.3010287.

"COVID-19 Radiography Database." https://kaggle.com/tawsifurrahman/covid19-radiography-database (accessed Oct. 28, 2021).

"COVID-19 X rays." https://kaggle.com/andrewmvd/convid19-x-rays (accessed Oct. 28, 2021).

X. Yang, X. He, J. Zhao, Y. Zhang, S. Zhang, and P. Xie, "COVID-CT-Dataset: A CT Scan Dataset about COVID-19," arXiv:2003.13865 [cs, eess, stat], Jun. 2020, Accessed: Oct. 28, 2021. [Online]. Available: http://arxiv.org/abs/2003.13865.

"COVID-19," Medical segmentation. http://medicalsegmentation.com/covid19/ (accessed Oct. 28, 2021).

"COVID-19 British Society of Thoracic Imaging Database | The British Society of Thoracic Imaging." https://www.bsti.org.uk/training-and-education/covid-19-bsti-imaging-database/ (accessed Oct. 28, 2021).

P. Chakraborty and C. Tharini, "Pneumonia and Eye Disease Detection using Convolutional Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5769–5774, Jun. 2020, https://doi.org/10.48084/etasr.3503.

H. H. A. Owida, A. Al-Ghraibah, and M. Altayeb, "Classification of Chest X-Ray Images using Wavelet and MFCC Features and Support Vector Machine Classifier," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7296–7301, Aug. 2021, https://doi.org/10.48084/etasr.4123.

N. K. Al-Shammari, H. B. Almansour, and M. B. Syed, "Development of an Automatic Contactless Thermometer Alert System Based on GPS and Population Density," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 7006–7010, Apr. 2021, https://doi.org/10.48084/etasr.4103.


How to Cite

H. Alalawi, M. Alsuwat, and H. Alhakami, “A Survey of the Application of Artifical Intellegence on COVID-19 Diagnosis and Prediction”, Eng. Technol. Appl. Sci. Res., vol. 11, no. 6, pp. 7824–7835, Dec. 2021.


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