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Machine Learning Techniques for COVID-19 Detection: A Comparative Analysis

Authors: Abeer Aljohani

Abstract:

The COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred as corona virus which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as Omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. Numerous COVID-19 cases have produced a huge burden on hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease based on the symptoms and medical history of the patient. As machine learning is a widely accepted area and gives promising results for healthcare, this research presents an architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard University of California Irvine (UCI) dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques on the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and Principal Component Analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, Receiver Operating Characteristic (ROC) and Area under Curve (AUC). The results depict that Decision tree, Random Forest and neural networks outperform all other state-of-the-art ML techniques. This result can be used to effectively identify COVID-19 infection cases.

Keywords: Supervised machine learning, COVID-19 prediction, healthcare analytics, Random Forest, Neural Network.

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References:


[1] E. Gambhir, R. Jain, A. Gupta and U. Tomer, “Regression analysis of COVID-19 using machine learning algorithms,” In: International conference on smart electronics and communication (ICOSEC), Tamil Nadu, India, 2020, pp. 65-71.
[2] S. Sakib, “Dl-crc: Deep learning-based chest radiograph classification for COVID-19 detection: A novel approach,” IEEE Access, 8, 2020, pp. 171575-171589.
[3] D. Weatherby and S. Ferguson, “Blood chemistry and CBC analysis,” Weatherby & Associates, LLC, 4, 2002, pp. 1-312.
[4] W. Ling, “C-reactive protein levels in the early stage of COVID-19,” Medecine et maladies infectieuses, 50(4), 2020, pp. 332-334.
[5] L. Zhang, X. Yan, Q. Fan, H. Liu, X. Liu, Z. Liu, Z. Zhang, “Ddimer levels on admission to predict in-hospital mortality in patients with COVID-19,” Journal of Thrombosis and Haemostasis, 18(6), 2020, pp. 1324-1329.
[6] S. TuralOnur, S. Altin, S. N. Sokucu, B. I. Fikri, T. Barca, E. Bolat and M. Toptas, “Could ferritin level be an indicator of COVID-19 disease mortality?,” Journal of medical virology, 93(3), 2020, pp. 1672-1677.
[7] H. Nyblom, U. Berggren, J. Balldin and R. Olsson, “High ast/alt ratio may indicate advanced alcoholic liver disease rather than heavy drinking,” Alcohol and alcoholism, 39(4), 2004, pp. 336-339.
[8] I. Serin, N. D. Sari, M. H. Dogu, S. D. Acikel, G. Babur, A. Ulusoy, M. I. Onar, E. C. Gokce, O. Altunok and F. Y. Mert, “A new parameter in COVID-19 pandemic: initial lactate dehydrogenase (ldh)/lymphocyte ratio for diagnosis and mortality,” Journal of Infection and Public Health, 13(11), 2020, pp. 1664-1670.
[9] D. Israni and H. Mewada, “Identity Retention of Multiple Objects under Extreme Occlusion Scenarios using Feature Descriptors,” Journal of Communications Software and Systems (JCOMSS), 14(4), 2018, 290-301.
[10] D. Israni and H. Mewada, “Feature descriptor based identity retention and tracking of players under intense occlusion in soccer videos,” International Journal of Intelligent Engineering and Systems, 11(4), 2018, pp. 31-41.
[11] N. Purohit and D. Israni, “Vehicle classification and surveillance using machine learning technique,” In: 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), India, 2017, pp. 910-914.
[12] P. Israni, “Breast cancer diagnosis (BCD) model using machine learning,” International Journal of Innovative Technology and Exploring Engineering, 8(10), 2019, pp. 4456-4463.
[13] Supervised vs. Unsupervised Learning: Key Differences. Available online: https://www.guru99.com/supervised-vsunsupervised- learning.html (accessed on 30 November 2021).
[14] L. P. Kaelbling, M. L. Littman and A. W. Moore, “Reinforcement Learning: A Survey,” Journal of Artificial Intelligence Research, 4, 1996, pp. 237–285.
[15] G. Monika and M. Bharathi Devi, “Using Machine Learning Approach to Predict COVID-19 Progress,” International Journal for Modern Trends in Science and Technology, 6(8S): 2020, pp. 58-62.
[16] R. Gupta, G. Pandey, P. Chaudhary and S. K. Pal, “Machine Learning Models for Government to Predict COVID-19 Outbreak,” Digital Government: Research and Practice, 1(4), 2020, pp. 1-6.
[17] C. Iwendi, A. K. Bashir, A. Peshkar, R. Sujatha, J. Chatterjee, S. Pasupuleti, R. Mishra, S. Pillai, and O. Jo, “COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm,” Frontiers in Public Health, 8, 2020, pp. 1-9.
[18] S. Makridakis, E. Spiliotis and V. Assimakopoulos, “Statistical and machine learning forecast ing methods: Concerns and ways forward,” PloS one, 13(3), 2018, pp. 1-26.
[19] https://www.kaggle.com/einsteindata4u/COVID19, last accessed: 2020/10/15.
[20] A. F. M. Batista, J. L. Miraglia, T. H. R. Donato and A. D. P. C. Filho, “COVID-19 diagnosis prediction in emergency care patients: a machine learning approach,” medRxiv, (2020).
[21] Y. Sun, V. Koh, K. Marimuthu, O. T. Ng, B. Young, S. Vasoo, M. Chan, V. J. Lee, P. P. De, T. Barkham and R. T. Lin et al., “Epidemiological and clinical predictors of COVID-19,” Clinical Infectious Disease, 71(15), 2020, pp. 786-792.
[22] Z. Meng, M. Wang, H. Song, S. Guo, Y. Zhou, W. Li, X. Song, Y. Zhou, and Q. Li, “Development and utilization of an intelligent application for aiding COVID-19 diagnosis,” medRxiv, 2020, pp. 1-21.
[23] L. Wang and A. Wong, “COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images,” Scientific Reports, 10(1), 2020, pp. 1-12.
[24] R. Pal, A. A. Sekh, S. Kar and D. K. Prasad, “Neural network-based country wise risk prediction of COVID-19,” Applied Sciences, 10(18), 2020, pp. 1-16.
[25] D. Liu, L. Clemente, C. Poirier, X. Ding, M. Chinazzi, J. T. Davis, A. Vespignani and M. Santillana, “A machine learning methodology for real-time forecasting of the 2019–2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models,” arXiv, 2020, pp. 1-23.
[26] C. Bayes and L. Valdivieso, “Modelling death rates due to COVID-19: a Bayesian approach,” arXiv, (2020).
[27] B. R. Beck, B. Shin, Y. Choi, S. Park and K. Kang, “Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model,” Computational and Structural Biotechnology Journal, 18, 2020, pp. 784-790.
[28] Z. Tang, W. Zhao, X. Xie, Z. Zhong, F. Shi, T. Ma, J. Liu and D. Shen, “Severity assessment of COVID-19 using CT image features and laboratory indices,” Physics in Medicine and Biology, 66(3), 2020, pp. 035015.
[29] N. E. M. Khalifa, M. H. N. Taha, A. E. Hassanien, and S. Elghamrawy, “Detection of coronavirus (COVID-19) associated pneumonia based on generative adversarial networks and a fine-tuned deep transfer learning model using chest X-ray dataset,” arXiv, (2020).
[30] R. Sujatha, J. M. Chatterjee, and A. E. Hassanien, “A machine learning forecasting model for COVID-19 pandemic in India,” Stochastic Environmental Research Risk Assessment. 34, 2020, pp. 959-972.
[31] A. Waheed, M. Goyal, D. Gupta, A. Khanna, F. Al-Turjman, and P. R. Pinheiro, “COVIDgan: data augmentation using auxiliary classifier GAN for improved COVID-19 detection,” IEEE Access, 8, 2020, pp. 91916–91923.
[32] J. Wu, P. Zhang, L. Zhang, W. Meng, J. Li, C. Tong, Y. Li, J. Cai, Z. Yang and J. Zhu et al., “Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results,” MedRxiv, 2020, pp. 1-12.
[33] A. Bastug, H. Bodur, S. Erdogan, D. Gokcinar, S. Kazancioglu, B. D. Kosovali, B. O. Ozbay, G. Gok, I. O. Turan, G. Yilmaz and C. C. Gonen, “Clinical and laboratory features of COVID-19: Predictors of severe prognosis,” International immunopharmacology, 88, 2020, 106950.
[34] D. Brinati, A. Campagner, D. Ferrari, M. Locatelli, G. Banfi, F. Cabitza, “Detection of COVID-19 infection from routine blood exams with machine learning: a feasibility study,” Journal of medical systems, 44(8), 2020, pp. 1-12.
[35] A. Mohamed-Hussein, I. Galal, M. M. A. R. Mohamed, H. A. Elaal and K. M. Aly, “Is there a correlation between pulmonary inflammation index with COVID-19 disease severity and outcome?,” medRxiv, (2020).
[36] Symptoms and COVID Presence (May 2020 data), https://www.kaggle.com/hemanthhari/symptoms-and-COVID-presence?select=COVID+Dataset.csv, last accessed: 2021/11/30.
[37] C. N. Villavicencio, J. J. E. Macrohon, X. A. Inbaraj, J. H. Jeng, and J. G. Hsieh, “COVID-19 Prediction applying supervised machine learning algorithms with comparative analysis using WEKA,” Algorithms, 14(7), 2021, pp. 201.