Disease Forecasting and Severity Prediction Model for COVID-19 Using Correlated Feature Extraction and Feed-Forward Artificial Neural Networks
How to Cite?
Jayaraj T, Dr. J. Abdul Samath, "Disease Forecasting and Severity Prediction Model for COVID-19 Using Correlated Feature Extraction and Feed-Forward Artificial Neural Networks," International Journal of Engineering Trends and Technology, vol. 69, no. 8, pp. 126-137, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I8P216
Abstract
The digitization of the medical sector has led to an explosion of heterogeneous medical records. The contribution of big data in the medical field is used to effectively address certain unsolved issues. Effectively integrating and analyzing this big data can reveal many useful hidden medical information. The COVID-19 virus, which first appeared in China at the end of 2019, is suffocating the rest of the world. Traditional methods of preventing this unforeseen pandemic are the lengthy process that can take several years. Now, the second wave of COVID-19 is wreaking havoc throughout the world. The survival rate can be significantly increased by predicting the risk of COVID-19 infection based on the patient’s early symptoms and health status. Various disease forecasting approaches based on machine learning, and deep learning has been developed to forecast the severity rate of COVID- 19. However, these approaches are becoming useless as the virus mutates. In this proposed research, the prediction model is trained by the proposed Correlated Feature Extraction (CFE) method according to the virus behaviors. Furthermore, two prediction models were established in this study. The first model uses initial symptoms to forecast positive cases. Second, based on the health status of the positive cases, the risk rate is estimated. In this study, the disease is forecast by Feed-Forward Artificial Neural Networks (FFANN). Finally, a comparative study has been conducted with the recently developed COVID-19 disease prediction methods to demonstrate the training efficacy and accuracy of the proposed COVID-19 forecasting system.
Keywords
COVID-19 disease prediction, Big data, Artificial intelligence, Disease control, Expert system.
Reference
[1] L. Yan, H.-T. Zhang, Y. Xiao, et al., Prediction of criticality in patients with severe COVID-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan, medRxiv, (2020).
[2] Sumayh S. Aljameel, Irfan Ullah Khan, Nida Aslam, Malak Aljabri, Eman S. Alsulmi, Machine Learning-Based Model to Predict the Disease Severity and Outcome in COVID-19 Patients, Scientific Programming, (2021) Article ID 5587188, 10 pages, 2021. https://doi.org/10.1155/2021/5587188.
[3] Alotaibi, A.; Shiblee, M.; Alshahrani, A. Prediction of Severity of COVID-19-Infected Patients Using Machine Learning Techniques. Computers 10(2021) 31. https://doi.org/10.3390/computers10030031.
[4] Sánchez-Montañés, M.; Rodríguez-Belenguer, P.; Serrano-López, A.J.; Soria-Olivas, E.; Alakhdar-Mohmara, Y. Machine Learning for Mortality Analysis in Patients with COVID-19. Int. J. Environ. Res. Public Health 17(2020) 8386.
[5] Dash, S., Shakyawar, S.K., Sharma, M. et al. Big data in healthcare: management, analysis, and future prospects. J Big Data 6(54) (2019).
[6] Ristevski B, Chen M. Big Data Analytics in Medicine and Healthcare. J Integr Bioinform. 15(3) (2018) 20170030. Published 2018 May 10. doi:10.1515/jib-2017-0030.
[7] Ristevski B, Chen M. Big Data Analytics in Medicine and Healthcare. J Integr Bioinform. 15(3) (2018) 20170030. Published 2018 May 10. doi:10.1515/jib-2017-0030
[8] A. Krithara et al., iASiS: Towards Heterogeneous Big Data Analysis for Personalized Medicine, 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), (2019) 106-111, doi: 10.1109/CBMS.2019.00032.
[9] S. Kumar and M. Singh., Big data analytics for the healthcare industry: impact, applications, and tools, in Big Data Mining and Analytics, 2(1) (2019) 48-57, March 2019, doi: 10.26599/BDMA.2018.9020031.
[10] Singhal, T. A Review of Coronavirus Disease-2019 (COVID-19). Indian J Pediatr 87 (2020) 281–286. https://doi.org/10.1007/s12098- 020-03263-6.
[11] V. Chamola, V. Hassija, V. Gupta and M. Guizani., A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact., in IEEE Access, 90225-90265, 8(2020) doi: 10.1109/ACCESS.2020.2992341.
[12] T. Xin., The Model of COVID-19 Pandemic, International Conference on Computing and Data Science (CDS), (2020) 429-432, doi: 10.1109/CDS49703.2020.00090.
[13] Lowe, D., Tipping, M. Feed-forward neural networks and topographic mappings for exploratory data analysis. Neural Comput & Applic 4 (1996) 83–95 (1996). https://doi.org/10.1007/BF01413744.
[14] Mi?ušík, D., Stopjaková, V. & Be?ušková, L. Application of Feedforward Artificial Neural Networks to the Identification of Defective Analog Integrated Circuits. Neural Comput Applic 11 (2002) 71–79. https://doi.org/10.1007/s005210200018.
[15] Hecht-Nielsen., Theory of the backpropagation neural network., International Joint Conference on Neural Networks, 1(1989) 593- 605 doi: 10.1109/IJCNN.1989.118638.
[16] M. Roopa and S. S. K. Raja., Artificial neural network using backpropagation algorithm in distributed MANETs., International Conference on Information Communication and Embedded Systems (ICICES), (2016) 1-4.
[17] N. B. M. Khairudin, N. B. Mustapha, T. N. B. M. Aris, and M. B. Zolkepli., Comparison of Machine Learning Models For Rainfall Forecasting, International Conference on Computer Science and Its Application in Agriculture (ICOSICA), (2020) 1-5, doi: 10.1109/ICOSICA49951.2020.9243275.
[18] S. I. Popoola et al., Determination of Neural Network Parameters for Path Loss Prediction in Very High-Frequency Wireless Channel, in IEEE Access, 150462-150483, 7(2019) doi: 10.1109/ACCESS.2019.2947009.
[19] S.Sunitha, Dr.S.S. Sujatha., Combined Feature Learning And CNN For Polyp Detection In Wireless Capsule Endoscopy Images., International Journal of Engineering Trends and Technology 69(6)(2021) 206-215.