A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients
https://doi.org/10.24017/science.2021.2.5
Abstract views: 895 / PDF downloads: 682Abstract
COVID-19, one of the most dangerous pandemics, is currently affecting humanity. COVID-19 is spreading rapidly due to its high reliability transmissibility. Patients who test positive more often have mild to severe symptoms such as a cough, fever, raw throat, and muscle aches. Diseased people experience severe symptoms in more severe cases. such as shortness of breath, which can lead to respiratory failure and death. Machine learning techniques for detection and classification are commonly used in current medical diagnoses. However, for treatment using neural networks based on improved Particle Swarm Optimization (PSO), known as PSONN, the accuracy and performance of current models must be improved. This hybridization implements Particle Swarm Optimization and a neural network to improve results while slowing convergence and improving efficiency. The purpose of this study is to contribute to resolving this issue by presenting the implementation and assessment of Machine Learning models. Using Neural Networks and Particle Swarm Optimization to help in the detection of COVID-19 in its early stages. To begin, we preprocessed data from a Brazilian dataset consisted primarily of early-stage symptoms. Following that, we implemented Neural Network and Particle Swarm Optimization algorithms. We used precision, accuracy score, recall, and F-Measure tests to evaluate the Neural Network with Particle Swarm Optimization algorithms. Based on the comparison, this paper grouped the top seven ML models such as Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks algorithms and measured feature importance, and other, to justify the differences between classification models. Particle Swarm Optimization with Neural Network is being deployed to improve the efficiency of the detection method by more accurately predicting COVID-19 detection. Preprocessed datasets with important features are then fed into the testing and training phases as inputs. Particle Swarm Optimization was used for the training phase of a neural net to identify the best weights and biases. On training data, the highest rate of accuracy gained is 0.98.738 and on testing data, it is 98.689.
Keywords:
Particle Swarm Optimization, Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks.
References
[1] A. Altan and S. Karasu, "Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique," Chaos, Solitons & Fractals, vol. 140, p. 110071, 2020.
https://doi.org/10.1016/j.chaos.2020.110071
[2] WHO, "Coronavirus disease (COVID-19) pandemic," World Health Organization, vol. 2019, p. 2633, 2020.
[3] D. Worldometer, "COVID-19 coronavirus pandemic," World Health Organization, www. worldometers. info,2020.
[4] T. R. C. Group, "Dexamethasone in hospitalized patients with Covid-19-preliminary report," The New England journal of medicine, 2020.
[5] J. Grein, N. Ohmagari, D. Shin, G. Diaz, E. Asperges, A. Castagna, et al., "Compassionate use of remdesivir for patients with severe Covid-19," New England Journal of Medicine, vol. 382, pp. 2327-2336, 2020.
https://doi.org/10.1056/NEJMc2015312
[6] H. Bauchner and P. B. Fontanarosa, "Randomized clinical trials and COVID-19: managing expectations," Jama, vol. 323, pp. 2262-2263, 2020.
https://doi.org/10.1001/jama.2020.8115
[7] M. A. Matthay and B. T. Thompson, "Dexamethasone in hospitalised patients with COVID-19: addressing uncertainties," The Lancet Respiratory Medicine, vol. 8, pp. 1170-1172, 2020.
https://doi.org/10.1016/S2213-2600(20)30503-8
[8] G. P. Mishra and J. Mulani, "Corticosteroids for COVID-19: the search for an optimum duration of therapy," The Lancet. Respiratory Medicine, vol. 9, p. e8, 2021.
https://doi.org/10.1016/S2213-2600(20)30530-0
[9] J. Corum, D. Grady, S.-L. Wee, and C. Zimmer, "Coronavirus vaccine tracker," The New York Times, vol. 5, 2020.
[10] K. A. Moore, M. Lipsitch, J. M. Barry, and M. T. Osterholm, "COVID-19: the CIDRAP viewpoint," Center for Infectious Disease Research and Policy (CIDRAP), 2020.
[11] G. Guo, L. Ye, K. Pan, Y. Chen, D. Xing, K. Yan, et al., "New insights of emerging SARS-CoV-2: epidemiology, etiology, clinical features, clinical treatment, and prevention," Frontiers in Cell and Developmental Biology, vol. 8, p. 410, 2020.
https://doi.org/10.3389/fcell.2020.00410
[12] N. Chen, M. Zhou, X. Dong, J. Qu, F. Gong, Y. Han, et al., "Características epidemiológicas y clínicas de 99 casos de neumonía por el nuevo coronavirus de 2019 en Wuhan, China: un estudio descriptivo," Lancet, vol. 395, pp. 507-513, 2020.
[13] A. M. U. D. Khanday, S. T. Rabani, Q. R. Khan, N. Rouf, and M. M. U. Din, "Machine learning based approaches for detecting COVID-19 using clinical text data," International Journal of Information Technology, vol. 12, pp. 731-739, 2020.
https://doi.org/10.1007/s41870-020-00495-9
[14] S. Dhamodharavadhani, R. Rathipriya, and J. M. Chatterjee, "COVID-19 mortality rate prediction for India using statistical neural network models," Frontiers in Public Health, vol. 8, 2020.
https://doi.org/10.3389/fpubh.2020.00441
[15] T. Arifin, "Implementasi Algoritma PSO Dan Teknik Bagging Untuk Klasifikasi Sel Pap Smear," Jurnal Informatika, vol. 4, 2017.
[16] Y. Zhang, D.-w. Gong, and J. Cheng, "Multi-objective particle swarm optimization approach for cost-based feature selection in classification," IEEE/ACM transactions on computational biology and bioinformatics, vol. 14, pp. 64-75, 2015.
https://doi.org/10.1109/TCBB.2015.2476796
[17] R. Richman and M. V. Wüthrich, "A neural network extension of the Lee-Carter model to multiple populations," Annals of Actuarial Science, vol. 15, pp. 346-366, 2021.
https://doi.org/10.1017/S1748499519000071
[18] F. Fekrazad, "A best approach in intrusion detection for computer network PNN/GRNN/RBF," International Journal of Computer Science Issues (IJCSI), vol. 11, p. 182, 2014.
[19] P. Jeatrakul and K. W. Wong, "Comparing the performance of different neural networks for binary classification problems," in 2009 Eighth International Symposium on Natural Language Processing, 2009, pp. 111-115.
https://doi.org/10.1109/SNLP.2009.5340935
[20] M. Wo?niak, D. Po?ap, G. Capizzi, G. L. Sciuto, L. Ko?mider, and K. Frankiewicz, "Small lung nodules detection based on local variance analysis and probabilistic neural network," Computer methods and programs in biomedicine, vol. 161, pp. 173-180, 2018.
https://doi.org/10.1016/j.cmpb.2018.04.025
[21] W. Jia, X. Li, K. Tan, and G. Xie, "Predicting the outbreak of the hand-foot-mouth diseases in China using recurrent neural network," in 2019 IEEE International Conference on Healthcare Informatics (ICHI), 2019, pp. 1-4.
https://doi.org/10.1109/ICHI.2019.8904736
[22] W. B. Hamer, T. Birr, J.-A. Verreet, R. Duttmann, and H. Klink, "Spatio-temporal prediction of the epidemic spread of dangerous pathogens using machine learning methods," ISPRS International Journal of GeoInformation, vol. 9, p. 44, 2020.
https://doi.org/10.3390/ijgi9010044
[23] S. Mezzatesta, C. Torino, P. De Meo, G. Fiumara, and A. Vilasi, "A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis," Computer methods and programs in biomedicine, vol. 177, pp. 9-15, 2019.
https://doi.org/10.1016/j.cmpb.2019.05.005
[24] S.-L. Jhuo, M.-T. Hsieh, T.-C. Weng, M.-J. Chen, C.-M. Yang, and C.-H. Yeh, "Trend prediction of influenza and the associated pneumonia in taiwan using machine learning," in 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2019, pp. 1-2.
https://doi.org/10.1109/ISPACS48206.2019.8986244
[25] S. Kumar, V. Suresh, B. Reddy, and Y. Reddy, "Outbreak predictions in healthcare domain using machine learning & artificial intelligence," TEST Eng. Manag, vol. 82, pp. 11395-11400, 2020.
[26] G. Machado, C. Vilalta, M. Recamonde-Mendoza, C. Corzo, M. Torremorell, A. Perez, et al., "Identifying outbreaks of Porcine Epidemic Diarrhea virus through animal movements and spatial neighborhoods," Scientific reports, vol. 9, pp. 1-12, 2019.
https://doi.org/10.1038/s41598-018-36934-8
[27] Q. Ke, J. Zhang, W. Wei, D. Po?ap, M. Wo?niak, L. Ko?mider, et al., "A neuro-heuristic approach for recognition of lung diseases from X-ray images," Expert Systems with Applications, vol. 126, pp. 218-232, 2019.
https://doi.org/10.1016/j.eswa.2019.01.060
[28] J. Chen, L. Wu, J. Zhang, L. Zhang, D. Gong, Y. Zhao, et al., "Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography," Scientific reports, vol. 10, pp. 1-11, 2020.
https://doi.org/10.1038/s41598-020-76282-0
[29] Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, "Unet++: A nested u-net architecture for medical image segmentation," in Deep learning in medical image analysis and multimodal learning for clinical decision support, ed: Springer, 2018, pp. 3-11.
https://doi.org/10.1007/978-3-030-00889-5_1
[30] 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, pp. 1-12, 2020.
https://doi.org/10.1038/s41598-020-76550-z
[31] S. H. Yoon, K. H. Lee, J. Y. Kim, Y. K. Lee, H. Ko, K. H. Kim, et al., "Chest radiographic and CT findings of the 2019 novel coronavirus disease (COVID-19): analysis of nine patients treated in Korea," Korean journal of radiology, vol. 21, pp. 494-500, 2020.
https://doi.org/10.3348/kjr.2020.0132
[32] P. K. Sethy and S. K. Behera, "Detection of coronavirus disease (covid-19) based on deep features," 2020.
https://doi.org/10.20944/preprints202003.0300.v1
[33] Y. Wang, C. Xu, Y. Li, W. Wu, L. Gui, J. Ren, et al., "An advanced data-driven hybrid model of SARIMANNNAR for tuberculosis incidence time series forecasting in Qinghai Province, China," Infection and drug resistance, vol. 13, p. 867, 2020.
https://doi.org/10.2147/IDR.S232854
[34] 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, pp. 635-640, 2020.
https://doi.org/10.1007/s13246-020-00865-4
[35] V. Chouhan, S. K. Singh, A. Khamparia, D. Gupta, P. Tiwari, C. Moreira, et al., "A novel transfer learning based approach for pneumonia detection in chest X-ray images," Applied Sciences, vol. 10, p. 559, 2020.
https://doi.org/10.3390/app10020559
[36] L. Li, L. Qin, Z. Xu, Y. Yin, X. Wang, B. Kong, et al., "Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT," Radiology, 2020.
[37] P. Afshar, S. Heidarian, F. Naderkhani, A. Oikonomou, K. N. Plataniotis, and A. Mohammadi, "Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images," Pattern Recognition Letters, vol. 138, pp. 638-643, 2020.
https://doi.org/10.1016/j.patrec.2020.09.010
[38] X. Mei, H.-C. Lee, K.-y. Diao, M. Huang, B. Lin, C. Liu, et al., "Artificial intelligence-enabled rapid diagnosis of patients with COVID-19," Nature medicine, vol. 26, pp. 1224-1228, 2020.
https://doi.org/10.1038/s41591-020-0931-3
[39] M. M. Ahamad, S. Aktar, M. Rashed-Al-Mahfuz, S. Uddin, P. Liò, H. Xu, et al., "A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients," Expert systems with applications, vol. 160, p. 113661, 2020.
https://doi.org/10.1016/j.eswa.2020.113661
[40] Y. Zoabi, S. Deri-Rozov, and N. Shomron, "Machine learning-based prediction of COVID-19 diagnosis based on symptoms," npj digital medicine, vol. 4, pp. 1-5, 2021.
https://doi.org/10.1038/s41746-020-00372-6
[41] A. A. Enughwure and I. C. Febaide, "Applications of artificial intelligence in combating Covid-19: a systematic review," Open Access Library Journal, vol. 7, pp. 1-12, 2020.
https://doi.org/10.4236/oalib.1106628
[42] S. Banik, S. Banik, A. Ghosh, and A. Mukherjee, "Probabilistic estimation of COVID-19 using patient's symptoms," in Data Driven Approach Towards Disruptive Technologies: Proceedings of MIDAS 2020, 2021, pp. 369-378.
https://doi.org/10.1007/978-981-15-9873-9_29
[43] Í. V. dos Santos Santana, A. C. da Silveira, Á. Sobrinho, L. C. e Silva, L. D. da Silva, D. F. Santos, et al., "Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach," Journal of medical Internet research, vol. 23, p. e27293, 2021.
https://doi.org/10.2196/27293
[44] S. Shilaskar and A. Ghatol, "Diagnosis system for imbalanced multi-minority medical dataset," Soft Computing, vol. 23, pp. 4789-4799, 2019.
https://doi.org/10.1007/s00500-018-3133-x
[45] W. H. Organization, "Gender and COVID-19: advocacy brief, 14 May 2020," World Health Organization2020.
[46] H. Peckham, N. M. de Gruijter, C. Raine, A. Radziszewska, C. Ciurtin, L. R. Wedderburn, et al., "Male sex identified by global COVID-19 meta-analysis as a risk factor for death and ITU admission," Nature communications, vol. 11, pp. 1-10, 2020.
https://doi.org/10.1038/s41467-020-19741-6
[47] B. A. Hassan and T. A. Rashid, "Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation," Appl. Math. Comput., p. 124919, 2019.
https://doi.org/10.1016/j.amc.2019.124919
[48] B. A. Hassan, "CSCF: a chaotic sine cosine firefly algorithm for practical application problems," Neural Comput. Appl., pp. 1-20, 2020.
https://doi.org/10.1007/s00521-020-05474-6
[49] B. A. Hassan and T. A. Rashid, "Datasets on statistical analysis and performance evaluation of backtracking search optimisation algorithm compared with its counterpart algorithms," Data Br., vol. 28, p. 105046, 2020.
https://doi.org/10.1016/j.dib.2019.105046
[50] H. K. Hamarashid, S. A. Saeed, and T. A. Rashid, "Next word prediction based on the N-gram model for Kurdish Sorani and Kurmanji," Neural Comput. Appl., vol. 33, no. 9, pp. 4547-4566, 2021.
https://doi.org/10.1007/s00521-020-05245-3
[51] B. A. Hassan and T. A. Rashid, "A multidisciplinary ensemble algorithm for clustering heterogeneous datasets," Neural Comput. Appl., 2021.
https://doi.org/10.1007/s00521-020-05649-1
[52] M. H. R. Saeed, B. A. Hassan, and S. M. Qader, "An Optimized Framework to Adopt Computer Laboratory Administrations for Operating System and Application Installations," Kurdistan J. Appl. Res., vol. 2, no. 3, pp. 92-97, 2017.
https://doi.org/10.24017/science.2017.3.8
[53] B. A. Hassan, A. M. Ahmed, S. A. Saeed, and A. A. Saeed, "Evaluating e-Government Services in Kurdistan Institution for Strategic Studies and Scientific Research Using the EGOVSAT Model," Kurdistan J. Appl. Res., vol. 1, no. 2, pp. 1-7, 2016.
https://doi.org/10.24017/science.2016.1.2.2
[54] H. K. Hamarashid, "Utilizing Statistical Tests for Comparing Machine Learning Algorithms," 2021.
https://doi.org/10.24017/science.2021.1.8
[55] B. A. Hassan, T. A. Rashid, and S. Mirjalili, "Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star," Complex Intell. Syst., pp. 1-16, 2021.
https://doi.org/10.1007/s40747-021-00422-w
[56] B. A. Hassan, T. A. Rashid, and S. Mirjalili, "Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets," Data Br., p. 107044, 2021.
https://doi.org/10.1016/j.dib.2021.107044
[57] H. Hamarashid and S. Saeed, "Usability Testing on Sulaimani Polytechnic University Website," Int. J. Multidiscip. Curr. Res., vol. 5, 2017.
[58] B. A. Hassan and S. M. Qader, "A New Framework to Adopt Multidimensional Databases for Organizational Information System Strategies," arXiv Prepr. arXiv2105.08131, 2021.
[59] B. A. Hassan, "Analysis for the overwhelming success of the web compared to microcosm and hyper-G systems," arXiv Prepr. arXiv2105.08057, 2021.
[60] B. Hassan and S. Dasmahapatra, "Towards Semantic Web: Challenges and Needs."
[61] S. Saeed, S. Nawroly, H. H. Rashid, and N. Ali, "Evaluating e-Court Services Using the Usability Tests Model Case Study: Civil Court Case Management," Kurdistan J. Appl. Res., vol. 1, no. 1, pp. 76-83, 2016.
https://doi.org/10.24017/science.2016.1.1.9
[62] H. K. Hamarashid, M. H. R. Saeed, and S. Saeed, "Designing a Smart Traffic Light Algorithm (HMS) Based on Modified Round Robin Algorithm," Kurdistan J. Appl. Res., vol. 2, no. 1, pp. 27-30, 2017.
https://doi.org/10.24017/science.2017.1.8
[63] B. A. Hassan, T. A. Rashid, and H. K. Hamarashid, "A Novel Cluster Detection of COVID-19 Patients and Medical Disease Conditions Using Improved Evolutionary Clustering Algorithm Star," Comput. Biol. Med., p. 104866, 2021.
https://doi.org/10.1016/j.compbiomed.2021.104866
https://doi.org/10.1016/j.chaos.2020.110071
[2] WHO, "Coronavirus disease (COVID-19) pandemic," World Health Organization, vol. 2019, p. 2633, 2020.
[3] D. Worldometer, "COVID-19 coronavirus pandemic," World Health Organization, www. worldometers. info,2020.
[4] T. R. C. Group, "Dexamethasone in hospitalized patients with Covid-19-preliminary report," The New England journal of medicine, 2020.
[5] J. Grein, N. Ohmagari, D. Shin, G. Diaz, E. Asperges, A. Castagna, et al., "Compassionate use of remdesivir for patients with severe Covid-19," New England Journal of Medicine, vol. 382, pp. 2327-2336, 2020.
https://doi.org/10.1056/NEJMc2015312
[6] H. Bauchner and P. B. Fontanarosa, "Randomized clinical trials and COVID-19: managing expectations," Jama, vol. 323, pp. 2262-2263, 2020.
https://doi.org/10.1001/jama.2020.8115
[7] M. A. Matthay and B. T. Thompson, "Dexamethasone in hospitalised patients with COVID-19: addressing uncertainties," The Lancet Respiratory Medicine, vol. 8, pp. 1170-1172, 2020.
https://doi.org/10.1016/S2213-2600(20)30503-8
[8] G. P. Mishra and J. Mulani, "Corticosteroids for COVID-19: the search for an optimum duration of therapy," The Lancet. Respiratory Medicine, vol. 9, p. e8, 2021.
https://doi.org/10.1016/S2213-2600(20)30530-0
[9] J. Corum, D. Grady, S.-L. Wee, and C. Zimmer, "Coronavirus vaccine tracker," The New York Times, vol. 5, 2020.
[10] K. A. Moore, M. Lipsitch, J. M. Barry, and M. T. Osterholm, "COVID-19: the CIDRAP viewpoint," Center for Infectious Disease Research and Policy (CIDRAP), 2020.
[11] G. Guo, L. Ye, K. Pan, Y. Chen, D. Xing, K. Yan, et al., "New insights of emerging SARS-CoV-2: epidemiology, etiology, clinical features, clinical treatment, and prevention," Frontiers in Cell and Developmental Biology, vol. 8, p. 410, 2020.
https://doi.org/10.3389/fcell.2020.00410
[12] N. Chen, M. Zhou, X. Dong, J. Qu, F. Gong, Y. Han, et al., "Características epidemiológicas y clínicas de 99 casos de neumonía por el nuevo coronavirus de 2019 en Wuhan, China: un estudio descriptivo," Lancet, vol. 395, pp. 507-513, 2020.
[13] A. M. U. D. Khanday, S. T. Rabani, Q. R. Khan, N. Rouf, and M. M. U. Din, "Machine learning based approaches for detecting COVID-19 using clinical text data," International Journal of Information Technology, vol. 12, pp. 731-739, 2020.
https://doi.org/10.1007/s41870-020-00495-9
[14] S. Dhamodharavadhani, R. Rathipriya, and J. M. Chatterjee, "COVID-19 mortality rate prediction for India using statistical neural network models," Frontiers in Public Health, vol. 8, 2020.
https://doi.org/10.3389/fpubh.2020.00441
[15] T. Arifin, "Implementasi Algoritma PSO Dan Teknik Bagging Untuk Klasifikasi Sel Pap Smear," Jurnal Informatika, vol. 4, 2017.
[16] Y. Zhang, D.-w. Gong, and J. Cheng, "Multi-objective particle swarm optimization approach for cost-based feature selection in classification," IEEE/ACM transactions on computational biology and bioinformatics, vol. 14, pp. 64-75, 2015.
https://doi.org/10.1109/TCBB.2015.2476796
[17] R. Richman and M. V. Wüthrich, "A neural network extension of the Lee-Carter model to multiple populations," Annals of Actuarial Science, vol. 15, pp. 346-366, 2021.
https://doi.org/10.1017/S1748499519000071
[18] F. Fekrazad, "A best approach in intrusion detection for computer network PNN/GRNN/RBF," International Journal of Computer Science Issues (IJCSI), vol. 11, p. 182, 2014.
[19] P. Jeatrakul and K. W. Wong, "Comparing the performance of different neural networks for binary classification problems," in 2009 Eighth International Symposium on Natural Language Processing, 2009, pp. 111-115.
https://doi.org/10.1109/SNLP.2009.5340935
[20] M. Wo?niak, D. Po?ap, G. Capizzi, G. L. Sciuto, L. Ko?mider, and K. Frankiewicz, "Small lung nodules detection based on local variance analysis and probabilistic neural network," Computer methods and programs in biomedicine, vol. 161, pp. 173-180, 2018.
https://doi.org/10.1016/j.cmpb.2018.04.025
[21] W. Jia, X. Li, K. Tan, and G. Xie, "Predicting the outbreak of the hand-foot-mouth diseases in China using recurrent neural network," in 2019 IEEE International Conference on Healthcare Informatics (ICHI), 2019, pp. 1-4.
https://doi.org/10.1109/ICHI.2019.8904736
[22] W. B. Hamer, T. Birr, J.-A. Verreet, R. Duttmann, and H. Klink, "Spatio-temporal prediction of the epidemic spread of dangerous pathogens using machine learning methods," ISPRS International Journal of GeoInformation, vol. 9, p. 44, 2020.
https://doi.org/10.3390/ijgi9010044
[23] S. Mezzatesta, C. Torino, P. De Meo, G. Fiumara, and A. Vilasi, "A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis," Computer methods and programs in biomedicine, vol. 177, pp. 9-15, 2019.
https://doi.org/10.1016/j.cmpb.2019.05.005
[24] S.-L. Jhuo, M.-T. Hsieh, T.-C. Weng, M.-J. Chen, C.-M. Yang, and C.-H. Yeh, "Trend prediction of influenza and the associated pneumonia in taiwan using machine learning," in 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2019, pp. 1-2.
https://doi.org/10.1109/ISPACS48206.2019.8986244
[25] S. Kumar, V. Suresh, B. Reddy, and Y. Reddy, "Outbreak predictions in healthcare domain using machine learning & artificial intelligence," TEST Eng. Manag, vol. 82, pp. 11395-11400, 2020.
[26] G. Machado, C. Vilalta, M. Recamonde-Mendoza, C. Corzo, M. Torremorell, A. Perez, et al., "Identifying outbreaks of Porcine Epidemic Diarrhea virus through animal movements and spatial neighborhoods," Scientific reports, vol. 9, pp. 1-12, 2019.
https://doi.org/10.1038/s41598-018-36934-8
[27] Q. Ke, J. Zhang, W. Wei, D. Po?ap, M. Wo?niak, L. Ko?mider, et al., "A neuro-heuristic approach for recognition of lung diseases from X-ray images," Expert Systems with Applications, vol. 126, pp. 218-232, 2019.
https://doi.org/10.1016/j.eswa.2019.01.060
[28] J. Chen, L. Wu, J. Zhang, L. Zhang, D. Gong, Y. Zhao, et al., "Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography," Scientific reports, vol. 10, pp. 1-11, 2020.
https://doi.org/10.1038/s41598-020-76282-0
[29] Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, "Unet++: A nested u-net architecture for medical image segmentation," in Deep learning in medical image analysis and multimodal learning for clinical decision support, ed: Springer, 2018, pp. 3-11.
https://doi.org/10.1007/978-3-030-00889-5_1
[30] 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, pp. 1-12, 2020.
https://doi.org/10.1038/s41598-020-76550-z
[31] S. H. Yoon, K. H. Lee, J. Y. Kim, Y. K. Lee, H. Ko, K. H. Kim, et al., "Chest radiographic and CT findings of the 2019 novel coronavirus disease (COVID-19): analysis of nine patients treated in Korea," Korean journal of radiology, vol. 21, pp. 494-500, 2020.
https://doi.org/10.3348/kjr.2020.0132
[32] P. K. Sethy and S. K. Behera, "Detection of coronavirus disease (covid-19) based on deep features," 2020.
https://doi.org/10.20944/preprints202003.0300.v1
[33] Y. Wang, C. Xu, Y. Li, W. Wu, L. Gui, J. Ren, et al., "An advanced data-driven hybrid model of SARIMANNNAR for tuberculosis incidence time series forecasting in Qinghai Province, China," Infection and drug resistance, vol. 13, p. 867, 2020.
https://doi.org/10.2147/IDR.S232854
[34] 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, pp. 635-640, 2020.
https://doi.org/10.1007/s13246-020-00865-4
[35] V. Chouhan, S. K. Singh, A. Khamparia, D. Gupta, P. Tiwari, C. Moreira, et al., "A novel transfer learning based approach for pneumonia detection in chest X-ray images," Applied Sciences, vol. 10, p. 559, 2020.
https://doi.org/10.3390/app10020559
[36] L. Li, L. Qin, Z. Xu, Y. Yin, X. Wang, B. Kong, et al., "Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT," Radiology, 2020.
[37] P. Afshar, S. Heidarian, F. Naderkhani, A. Oikonomou, K. N. Plataniotis, and A. Mohammadi, "Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images," Pattern Recognition Letters, vol. 138, pp. 638-643, 2020.
https://doi.org/10.1016/j.patrec.2020.09.010
[38] X. Mei, H.-C. Lee, K.-y. Diao, M. Huang, B. Lin, C. Liu, et al., "Artificial intelligence-enabled rapid diagnosis of patients with COVID-19," Nature medicine, vol. 26, pp. 1224-1228, 2020.
https://doi.org/10.1038/s41591-020-0931-3
[39] M. M. Ahamad, S. Aktar, M. Rashed-Al-Mahfuz, S. Uddin, P. Liò, H. Xu, et al., "A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients," Expert systems with applications, vol. 160, p. 113661, 2020.
https://doi.org/10.1016/j.eswa.2020.113661
[40] Y. Zoabi, S. Deri-Rozov, and N. Shomron, "Machine learning-based prediction of COVID-19 diagnosis based on symptoms," npj digital medicine, vol. 4, pp. 1-5, 2021.
https://doi.org/10.1038/s41746-020-00372-6
[41] A. A. Enughwure and I. C. Febaide, "Applications of artificial intelligence in combating Covid-19: a systematic review," Open Access Library Journal, vol. 7, pp. 1-12, 2020.
https://doi.org/10.4236/oalib.1106628
[42] S. Banik, S. Banik, A. Ghosh, and A. Mukherjee, "Probabilistic estimation of COVID-19 using patient's symptoms," in Data Driven Approach Towards Disruptive Technologies: Proceedings of MIDAS 2020, 2021, pp. 369-378.
https://doi.org/10.1007/978-981-15-9873-9_29
[43] Í. V. dos Santos Santana, A. C. da Silveira, Á. Sobrinho, L. C. e Silva, L. D. da Silva, D. F. Santos, et al., "Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach," Journal of medical Internet research, vol. 23, p. e27293, 2021.
https://doi.org/10.2196/27293
[44] S. Shilaskar and A. Ghatol, "Diagnosis system for imbalanced multi-minority medical dataset," Soft Computing, vol. 23, pp. 4789-4799, 2019.
https://doi.org/10.1007/s00500-018-3133-x
[45] W. H. Organization, "Gender and COVID-19: advocacy brief, 14 May 2020," World Health Organization2020.
[46] H. Peckham, N. M. de Gruijter, C. Raine, A. Radziszewska, C. Ciurtin, L. R. Wedderburn, et al., "Male sex identified by global COVID-19 meta-analysis as a risk factor for death and ITU admission," Nature communications, vol. 11, pp. 1-10, 2020.
https://doi.org/10.1038/s41467-020-19741-6
[47] B. A. Hassan and T. A. Rashid, "Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation," Appl. Math. Comput., p. 124919, 2019.
https://doi.org/10.1016/j.amc.2019.124919
[48] B. A. Hassan, "CSCF: a chaotic sine cosine firefly algorithm for practical application problems," Neural Comput. Appl., pp. 1-20, 2020.
https://doi.org/10.1007/s00521-020-05474-6
[49] B. A. Hassan and T. A. Rashid, "Datasets on statistical analysis and performance evaluation of backtracking search optimisation algorithm compared with its counterpart algorithms," Data Br., vol. 28, p. 105046, 2020.
https://doi.org/10.1016/j.dib.2019.105046
[50] H. K. Hamarashid, S. A. Saeed, and T. A. Rashid, "Next word prediction based on the N-gram model for Kurdish Sorani and Kurmanji," Neural Comput. Appl., vol. 33, no. 9, pp. 4547-4566, 2021.
https://doi.org/10.1007/s00521-020-05245-3
[51] B. A. Hassan and T. A. Rashid, "A multidisciplinary ensemble algorithm for clustering heterogeneous datasets," Neural Comput. Appl., 2021.
https://doi.org/10.1007/s00521-020-05649-1
[52] M. H. R. Saeed, B. A. Hassan, and S. M. Qader, "An Optimized Framework to Adopt Computer Laboratory Administrations for Operating System and Application Installations," Kurdistan J. Appl. Res., vol. 2, no. 3, pp. 92-97, 2017.
https://doi.org/10.24017/science.2017.3.8
[53] B. A. Hassan, A. M. Ahmed, S. A. Saeed, and A. A. Saeed, "Evaluating e-Government Services in Kurdistan Institution for Strategic Studies and Scientific Research Using the EGOVSAT Model," Kurdistan J. Appl. Res., vol. 1, no. 2, pp. 1-7, 2016.
https://doi.org/10.24017/science.2016.1.2.2
[54] H. K. Hamarashid, "Utilizing Statistical Tests for Comparing Machine Learning Algorithms," 2021.
https://doi.org/10.24017/science.2021.1.8
[55] B. A. Hassan, T. A. Rashid, and S. Mirjalili, "Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star," Complex Intell. Syst., pp. 1-16, 2021.
https://doi.org/10.1007/s40747-021-00422-w
[56] B. A. Hassan, T. A. Rashid, and S. Mirjalili, "Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets," Data Br., p. 107044, 2021.
https://doi.org/10.1016/j.dib.2021.107044
[57] H. Hamarashid and S. Saeed, "Usability Testing on Sulaimani Polytechnic University Website," Int. J. Multidiscip. Curr. Res., vol. 5, 2017.
[58] B. A. Hassan and S. M. Qader, "A New Framework to Adopt Multidimensional Databases for Organizational Information System Strategies," arXiv Prepr. arXiv2105.08131, 2021.
[59] B. A. Hassan, "Analysis for the overwhelming success of the web compared to microcosm and hyper-G systems," arXiv Prepr. arXiv2105.08057, 2021.
[60] B. Hassan and S. Dasmahapatra, "Towards Semantic Web: Challenges and Needs."
[61] S. Saeed, S. Nawroly, H. H. Rashid, and N. Ali, "Evaluating e-Court Services Using the Usability Tests Model Case Study: Civil Court Case Management," Kurdistan J. Appl. Res., vol. 1, no. 1, pp. 76-83, 2016.
https://doi.org/10.24017/science.2016.1.1.9
[62] H. K. Hamarashid, M. H. R. Saeed, and S. Saeed, "Designing a Smart Traffic Light Algorithm (HMS) Based on Modified Round Robin Algorithm," Kurdistan J. Appl. Res., vol. 2, no. 1, pp. 27-30, 2017.
https://doi.org/10.24017/science.2017.1.8
[63] B. A. Hassan, T. A. Rashid, and H. K. Hamarashid, "A Novel Cluster Detection of COVID-19 Patients and Medical Disease Conditions Using Improved Evolutionary Clustering Algorithm Star," Comput. Biol. Med., p. 104866, 2021.
https://doi.org/10.1016/j.compbiomed.2021.104866
Downloads
How to Cite
[1]
A. A. Hassan and T. A. Rashid, “A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients”, KJAR, vol. 6, no. 2, pp. 44–63, Dec. 2021, doi: 10.24017/science.2021.2.5.
Published
15-12-2021
Issue
Section
Pure and Applied Science
