A Review on Prediction of Covid-19 Cases using Machine Learning for Effective Public Health Management

Suberu Yusuf, Badamasi Imam Ya'u, Kabiru Musa Ibrahim, Muhammed Ali Bizi, Abdullahi Mukhtar Ahmed, Lawan Garba

Abstract


COVID-19 is a worldwide pandemic that has infected practically every country. At the moment, sustainable development in the field of public health is regarded as critical to ensuring a bright and prosperous future for people. However, prevalent disorders such as COVID-19 pose various hurdles to this endeavor, some of which are still unknown. A Shallow Single Layer Perceptron Neural Network (SSLPNN) and Gaussian Process Regression (GPR) model were used in this study for the classification and prediction of confirmed COVID-19 cases in five geographically diverse Asian regions with diverse settings and environmental conditions: China, South Korea, Japan, Saudi Arabia, and Pakistan. The input dataset included significant environmental and non-environmental factors, while the output dataset included confirmed COVID-19 cases. To discover patterns in the instances connected to fluctuations in the underlying variables, a correlation analysis was performed. This paper discusses some of the limitations of this model. The Shallow single layer perceptron neural network has a single layer of neurons and can only learn linear relationships between inputs and outputs. Convolutional Neural Network (CNN) methods will be introduced to address these issues. However, dynamic management has taken center stage in studies on the long-term growth of public health. However, dynamic management is dependent on proactive measures based on statistically validated methodologies, such as Artificial Intelligence (AI). In this study, a deep learning (CNN) model was trained to classify public health-related data, allowing the prediction of the number of confirmed COVID-19 cases in a particular location depending on parameter values. As a result, an autonomous prediction system aimed at determining the presence of COVID-19 in a person is required. A COVID-19 prediction model requires machine learning classification techniques, datasets, and machine learning software.


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Agrebi, S. and A. Larbi, (2020). “Use of artificial intelligence in infectious diseases,” Artificial Intelligence in Precision Health, pp. 415–438, 2020

Ahmad, F.; Saleh, N. A; Mamoona, H. S; Wasim, A. K. & Kashaf, J. (2021). Prediction of COVID-19 Casas using Machine Learning for Effective Public Health Management. Computers, Materials & Continua. DOI:10.32604/cmc.2021.013067.

Aljameel, S. S, I. U. Khan, N. A. slam, M. Aljabri, E. S. Alsulmi (2021). Machine Learning-BasedModel to Predict the Disease Severity and Outcome in COVID-19 Patients l; saljameel@iau.edu.sa Received 1 February 2021; Revised 6 March 2021; Accepted 10 April 2021; Published 20 April 2021

Anthimopoulos, M. S. Christodoulidis, L. Ebner, A. Christe and S. Mougiakakou (2016), "Lung pattern classification for interstitial lung diseases using a deep convolutional neural network", IEEE Trans. Med. Imag., vol. 35, no. 5, pp. 1207-1216, May 2016. Show in Context View Article

Ardabili, S. F., A. Mosavi, P. Ghamisi, F. Ferdinand, Annamaria R. Varkonyi-Koczy, U. Reuter, T. Rabczuk, M. Atkinson (2020). Machine learning. Multi layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS) model. Received: 8 September 2020; Accepted: 27 September 2020; Published: MDPI 1, october,2020.

Arslan, H. & Hassan, A. (2021). A new COVID-19 Detection Method from Human Genome Sequences using CPG Island features & KNN Classifier. Engineering Science and Technology an international Journal 24(2021)839-847.

Baker, T. S. F. Dutheil, and V. Navel,(2020). “COVID-19 as a factor influencing air pollution?,” Environmental Pollution, vol. 263, pp. 114466, 2020.

Bejnordi, B. E. (2017). "Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer", J. Amer. Med. Assoc., vol. 318, no. 22, pp. 2199-2210, 2017.Show in Context CrossRef Google Scholar

Bezirtzoglou., C Dekas.K and Charvalos,.E (2011).“Climate changes, environment and infection: Facts, scenarios and growing awareness from the public health community within Europe,” Anaerobe, vol. 17, no. 6, pp. 337–340, 2011

Bourzac. K. (2013). "The computer will see you now", Nature, vol. 502, no. 3, pp. S92-S94, 2013.Show in Context CrossRef Google Scholar

Brauer, (2010). “How much, how long, what, and where: Air pollution exposure assessment for epidemiologic studies of respiratory disease,” in Proc. of the American Thoracic Society, vol. 7, no. 2, pp. 111–115, 2010.

Chen,B H. Liang, X. Yuan, Y. Hu, M. Xu et al., “Roles of meteorological conditions in COVID-19 transmission on a worldwide scale,” medRxiv, 2020.

Chen, Q. M. Z. He, P. L. Kinney, T. Li, C. Sun et al., “Short-and intermediate-term exposure to NO2 and mortality: A multi-county analysis in China,” Environmental Pollution, vol. 261, pp. 114165, 2020.

Chen, X. W. and X. Lin (2014.)"Big data deep learning: Challenges and perspectives", IEEE Access, vol. 2, pp. 514-525, 2014.Show in Context View Article

Collins, A. and Y. Yao (2018), "Machine learning approaches: Data integration for disease prediction and prognosis", Applied Comput. Genomics, pp. 137-141, 2018.Show in Context CrossRef Google Scholar

Chowdhury, E .R Q. S. U. Ibrahim, M. S. Bari, M. J. Alam, S. J. Dunachie et al., “The association between temperature, rainfall and humidity with common climate-sensitive infectious diseases in Bangladesh,” PLoS One, vol. 13, no. 6, pp. 1–17, 2018

Cruz, J. A. and D. S. Wishart (2006), "Applications of machine learning in cancer prediction and prognosis", Cancer Informat., vol. 2, pp. 59-77, 2006.Show in Context CrossRef Google Scholar

Esteva, A. B. Kuppel, S. Thrun, (2017) "Dermatologist-level classification of skin cancer with deep neural networks"Nature, vol. 542, no. 7639, pp. 115 118, 2017. Show in context

Emrah, I. (2021). International Conference Data science and Applied (ICONDATA21)

Fatima M. and M. Pasha,(2017). "Survey of machine learning algorithms for disease diagnostic", J. Intell. Learn. Syst. Appl., vol. 9, no. 1, pp. 1-16, 2017.Show in Context CrossRef Google Scholar

Forna, A P. Nouvellet, I. Dorigatti and C. A. (2016).Donnelly, “Case fatality ratio estimates for the 2013–2016 West African Ebola epidemic: Application of boosted regression trees for imputation,” International Journal of Infectious Diseases, vol. 79, no. 12, pp. 128, 2019.

Garcia .C -Vidal, G. Sanjuan, P. Puerta-Alcalde, E. Moreno-García and A. Soriano, “Artificial intelligence to support clinical decision-making processes,” EBioMedicine, vol. 46, pp. 27–29, 2019.

Gulshan .V ,L.Peng, M. Coram, MC.Stumpe Wu.Jama(2016). "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs", J. Amer. Med. Assoc., vol. 316, no. 22, pp. 2402-2410, 2016. Show in Context CrossRef Google Scholar

Hassan T. and F. Ahmad, “Transaction and identity authentication security model for e-banking: Confluence of quantum cryptography and AI, in Int. Conf. on Intelligent Technologies and Applications, pp. 338–347, 2018.

Havaei, M. et al.,(2017). "Brain tumor segmentation with deep neural networks", Med. Image Anal., vol. 35, pp. 18-31, 2017.Show in Context CrossRef Google Scholar

Haykin, S. and N. Network, A comprehensive foundation. Neural networks, 2004. 2(2004): p. 41.

Huapaya . H. D, C. Rodrigue, D. Esenarro, (2020). Comparative analysis of supervised machine learning algorithms for heart disease detection. 3C Tecnología. Glosas de innovación aplicadas a la pyme. Edición Especial, Abril 2020, 233-247. http://doi.org/10.17993/3ctecno.2020

Jensen P. B,, L. J. Jensen and S. Brunak (2012), "Mining electronic health records: Towards better research applications and clinical care", Nature Rev. Genetics, vol. 13, no. 6, pp. 395-405, 2012. Show in Context CrossRef Google Scholar

Jua´rez A. Q , Armando T.-Go´mez , I. Hoyo-Ulloa , Roberto de J. Leo´n-Montiel, Al. B. U’Ren(2021). Identification of high-risk COVID-19 patients using machine learning. PLoS ONE 16(9): e0257234. https://doi.org/10.1371/journal.pone.0257234 Published: September 20, 2021

Laatifi, M.; Samira, D.; Abdulaziz, B; Hind E. ; Jaafar, J.; Younes, Zaid; Bonabid, E.O; Maria, N. (2022). Machine Learning Approaches in COVID-19 severity risk Prediction in Morocco. Journal of Big Data. https://doi.org/10.1986/s40537-021-00557-0.

Latif,. S J. Qadir, S. Farooq and M. Imran, (2017). "How 5G wireless (and concomitant technologies) will revolutionize healthcare", Future Internet, vol. 9, no. 4, pp. 93, 2017.Show in Context CrossRef Google Scholar

Lorenzoni, G. N. Sella , A. Boscolo , D. Azzolina1 , P. Bartolotta1 , L. Pasin , T. Pettenuzzo , Alessandro D. Cassai , F. Baratto , F. Toffoletto , S.De Rosa , G. Fullin , M.Peta , P. Rosi , E. Polati, A. Zanella, G. Grasselli, A.Pesenti, P. Navalesi, D. Gregor (2021). Journal of Anesthesia, Analgesia and Critical Care (2021) 1:3 https://doi.org/10.1186/s44158-021-00002

Majumdar, A. B.; Sombuhra, G.; Dharmpal, S. & Sourav, M. (2021). An Intelligent System for Prediction of COVID-19 Case using Machine Learning Framework- Logistic Regression. Journal of Physics 1797(2021) 012011. DOI: 10.1088/1742-6596/i/012011.

Martin, V. Chevalier, V. Ceccato, P. Anyamba, L. De Simone et al.(2008) .“The impact of climate change on the epidemiology and control of rift valley fever,” Revue Scientifique et Technique, vol. 27, no. 2, pp. 413–426, 2008.

Ma.Y, Y. Zhao, J. Liu, X. He, B. Wang et al., “Effects of temperature variation and humidity on the mortality of COVID-19 in Wuhan,” medRxiv, 2020.

Muhammad, L. J., Islam, M. M, Usman, S. S, (2020). Predictive data mining models for novel coronavirus (COVID-19) infected patients’ recovery. Springer Nat Comput Sci. 2020. https://doi.org/10.1007/ s42979-020-00216-w.

Nestor, M. McDermott, W. Boag,(2019). "Feature robustness in non-stationary health records: Caveats to deployable model performance in common clinical machine learning tasks", 2019.Show in Context Google Scholar

Philemon, M. D. Z. Ismail and J. Dare,(2019), “A review of epidemic forecasting using artificial neural networks,” International Journal of Epidemiology, vol. 6, no. 3, pp. 132–143, 2019.

Pirouz. B. Shaffiee, S. Haghshenas, S. Shaffiee Haghshenas and P. Piro, “Investigating a serious challenge in the sustainable development process: Analysis of confirmed cases of COVID-19 (new type of coronavirus) through a binary classification using artificial intelligence and regression analysis,” Sustainability, vol. 12, no. 6, pp. 2427, 2020.

Rajpurkar. P. (2017), "Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning", 2017.Show in Context Google Scholar

Rangarajan, A.; Krishnaswamy, R.; Krishnan, H. (2021) A preliminary analysis of AI based smartphone application for diagnosis of COVID-19 using chest X-ray images. Expert Syst. Appl. 2021, 183, 1–11.

Rees, E V. Ng, P. Gachon, A. Mawudeku, D. Mckenney et al., “Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change,” Canada Communicable Disease Reports, vol. 45, no. 5, pp. 119–126, 2019.

Satu, M. S.; Howlader, K.C.; Mahmud, M.; Kaiser, M.S.; Shariful Islam, S.M.; Quinn, J.M.W.; Alyami, S.A.; Moni, M.A. (2021). Short-Term Prediction of COVID-19 Cases Using Machine Learning Models. Appl. Sci. 2021, 11, 4266. https://doi.org/ 10.3390/app11094266

Schlemper, J. Caballero, J. V. Hajnal, A. Price and D. Rueckert, (2017). "A deep cascade of convolutional neural networks for MR image reconstruction", Proc. Int. Conf. Inf. Process. Med. Imag., pp. 647-658, 2017.Show in Context CrossRef Google Scholar

Shahzadi, S. B. Khaliq, M. Rizwan and F. Ahmad, “Security of cloud computing using adaptive neural fuzzy inference system,” Security and Communication Networks, vol. 2020, no. 8, pp. 1–15, 2020.

Shen, W, M. Zhou, F. Yang, C. Yang and J. Tian (2015). "Multi-scale convolutional neural networks for lung nodule classification", Proc. Int. Conf. Inf. Process. Med. Imag., pp. 588-599, 2015.Show in Context CrossRef Google Scholar

Temgoua, M. N.; Endomba, F.T.; Nkeck, J.R.; Kenfack, G.U.; Tochie, J.N.; Essouma, M. (2020). Coronavirus Disease 2019 (COVID-19) as a Multi-Systemic Disease and its Impact in Low- and Middle-Income Countries (LMICs). SN Compr. Clin. Med. 2020, 2, 1377–1387.

Thakur, A. & Konde .A (2021). Fundamental of Neural Networks, International journals for research in applied science of Engineering Technology (IJRASET) Vol. 9, Aug 2021. 2321-9653

Turabieh, H.; Karaa, W.B.A.(2021). Predicting the existence of COVID-19 using machine learning based on laboratory findings. In Proceedings of the 2021 International Conference of Women in Data Science at Taif University, Taif, Saudi Arabia, 30–31 March 2021.

Verhulst, P. F. Recherchesmath´ematiquessur la loid’accroissement de la population, M´em. Acad. R. Bruxelles 18 (1845), 3-39.

Villanceno, C. N; Macrohon, J. J. E; Inbarag, X. A’ Jeng, J. H’ Hsieh, J. G.(2021) COVID-19 Prediction Applying Supervised Machine Learning algorithm with Comparative Analysis usingWEKA.MDPIpublisherSwitzerlandVol.14(201).https://doi.org/10.3390/a114076201.

Wang. X. Peng, Y, Lu, Z. Lu and R. M. (2018) Summers, "Tienet: Text-image embedding network for common thorax disease classification and reporting in chest x-rays", Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 9049-9058, 2018.Show in Context View Article Google Scholar

Weng, S. F, J. Reps, J. Kai, J. M. Garibaldi and N. Qureshi, (2017). "Can machine-learning improve cardiovascular risk prediction using routine clinical data", PloS One, vol. 12, no. 4, 2017.Show in Context CrossRef Google Scholar

Xing, L. E. A. Krupinski and J. Cai, (2018)."Artificial intelligence will soon change the landscape of medical physics research and practice", Med. Phys., vol. 45, no. 5, pp. 1791-1793, 2018.Show in Context CrossRef Google Scholar

Xing, L. E. A. Krupinski and J. Cai, (2018)."Artificial intelligence will soon change the landscape of medical physics research and practice", Med. Phys., vol. 45, no. 5, pp. 1791-1793, 2018.Show in Context CrossRef Google Scholar

Xue, Y. (2018). "Multimodal recurrent model with attention for automated radiology report generation", Proc. Int. Conf. Med. Image Comput. Comput.-Assisted Intervention, pp. 457-466, 2018 Show in Context CrossRef Google Scholar

Yan, Z. (2016). "Multi-instance deep learning: Discover discriminative local anatomies for bodypart recognition", IEEE Trans. Med. Imag., vol. 35, no. 5, pp. 1332-1343, May 2016. Show in Context View Article Google Scholar

Yeh, P. Wu, T. Zhu, Z. Xiao, X. Zhang, L. Zheng, R. Zheng, Y. Sun, W. Zhou, Q. Fu, X. Ye, A. Chen , S. Zheng, A. A. Heidari , M. Wang, J. Zhu, H. Chen, (2021). Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods Received December 23, 2020, accepted January 15, 2021, date of publication January 19, 2021, date of current version February 1, 2021.

Zech, J. M. Pain, J. Titano, M. Badgele,, J. Schefflein, A. Anthony Costa, J. Bedreson, J. Lehar, E. Kael Qermann (2018)."Natural language–based machine learning models for the annotation of clinical radiology reports", Radiology, vol. 287, no. 2, pp. 570-580, 2018.Show in Context CrossRef Google Scholar

Zheng (2017). "A machine learning-based framework to identify type 2 diabetes through electronic health records", Int. J. Med. Informat., vol. 97, pp. 120-127,Show in Context CrossRef Google Scholar

Zhu, W. C. Liu, W. Fan and X. Xie, (2018)."Deeplung: Deep 3D dual path nets for automated pulmonary nodule detection and classification", Proc. IEEE Winter Conf. Appl. Comput. Vis., pp. 673-681, 2018.Show in Context View Article Google Scholar

Zoabi, Y.; Shira, D.R; Noam, S,(2020). Machine Learning-based on symptoms. Npj Digital Medicine (2021) 4.3. https://doi.org/10.1038/sa41746-020-00372-6.

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