Machine Learning Approaches for the Diagnosis of H1N1 and COVID-19
Keywords:
algorithms, classification, COVID-19, H1N1, machine learning, pandemicAbstract
COVID-19 and H1N1 are infections with similar symptoms that are often confused. These dis-eases cause adverse effects for individuals in the fields of economy, education, health, and technology. This study was planned to distinguish these two diseases by identifying the similarities between COVID-19 and H1N1 influenza. They are both pandemics that have caused significant distress worldwide. In this study, clinical data obtained from individuals diagnosed with H1N1 or COVID-19 were obtained for the analysis and an array of various machine learning algorithms were utilized for the categorization of that data. The results obtained from 23 different machine learning algorithms were compared and evaluated, indicating that our model success-fully classified the two diseases. The multilayer perceptron neural network algorithm displayed 95.87% accuracy. While sequential minimal optimization had 90.7% accuracy, the decision table algorithm had 90.91% accuracy. Using these three different algorithms, we achieved accuracy above 90% for the prediction of the studied diseases.
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References
World Health Organization (WHO). Available online: https://www.who.int/ (accessed on 26 March 2022).
Mayo Clinic. H1N1 flu (swine flu). Available online https://www.mayoclinic.org/diseases-conditions/swine-flu/symptoms-causes/syc-20378103 (accessed on 26 March 2022).
J. Alzubi, A. Nayyar, and A. Kumar, “Machine learning from theory to algorithms: an overview”, J. Phys. Conf. Ser., 1142, 2018.
E. Elbasi, A. Zreikat, S. Mathew and A. E. Topcu, “Classification of influenza H1N1 and COVID-19 patient data using machine learning”, 44th International Conference on Telecommunications and Signal Processing (TSP), Brno, Czech Republic, 26-28 July 2021.
Y. Pathak, P. K. Shukla and K. V. Arya, “Deep bidirectional classification model for COVID-19 disease infected patients”, IEEE/ACM Trans. Comput. Biol. Bioinform, 18, 1234-1241, 2021.
R. Y. Wang et al., “Predictions of COVID-19 infection severity based on co-associations between the SNPs of co-morbid diseases and COVID-19 through machine learning of genetic data”, IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT), Dalian, China, 20-22 November 2020.
J. E. Nalavade, “Digital screening tool to detect Covid-19 infected people”, International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 27-29 January 2021.
N. Vidushee and P. Rakhi, “Tracing the origins of COVID-19. In Use of AI, Robotics, and Modern Tools to Fight Covid-19” Jain, A., Sharma, A., Wang, J., Ram, M., Eds.; River Publishers: Gistrup, Denmark, 2021, pp. 17-28.
A. Jain et al., “Use of AI, robotics, and modern tools to fight Covid-19”, In Use of AI, Robotics, and Modern Tools to Fight Covid-19; Jain, A., Sharma, A., Wang, J., Ram, M., Eds.; River Publishers: Gistrup, Denmark, 2021.
S. Koç, G. Yilmaz and Y. Kabak, “The clinical guidelines usage towards the diagnosis and treatment of H1N1”, 5th International Symposium on Health Informatics and Bioinformatics, Ankara, Turkey, 20-22 April 2010.
S.M. Elembaby et al., “Gene regulatory network for H1N1 gene expression data”, 32nd National Radio Science Conference (NRSC), Giza, Egypt, 24-26 March 2015.
M. Lu and F. Li, “Survey on Lie group machine learning”, Big Data Min. Anal. 2020, 3, 235-258.
H. Arslan and B. Aygün, “Performance analysis of machine learning algorithms in detection of COVID-19 from common symptoms”, 29th Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkey, 9-11 June 2021.
A. Shamsi et al.,” An uncertainty-aware transfer learning-based framework for COVID-19 diagnosis”, IEEE Trans. Neural Network. Learn. Syst. 2021, 32, 1408-1417.
S. Tiwari, P. Chanak and S. K. Singh, “A review of the machine learning algorithms for Covid-19 case analysis”, IEEE Trans. Artificial Intelligent 2022.
A. Anupam, N.J. Mohan, S. Sahoo and S. Chakraborty, “Preliminary diagnosis of COVID-19 based on cough sounds using machine learning algorithms”, 5th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 6-8 May 2021.
M. Almansoor and N. M. Hewahi, “Exploring the relation between blood tests and Covid-19 using machine learning”, International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), Sakheer, Bahrain, 26-27 October 2020.
M. J. Farrukh, L. C. Ming, S. T. Zaidi and T. M. Khan, “Barriers and strategies to improve influenza vaccination in Pakistan”, Journal Infect. Public Health 2017, 10, 881-883.
G. Grasselli et. al., COVID-19 Lombardy ICU Network. Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy region, Italy. JAMA 2020, 323, 1574-1581.
S. A. Hashemi et. al., “High prevalence of SARS‐CoV‐2 and influenza A virus (H1N1) coinfection in dead patients in northeastern Iran”, Journal Medical Virol. 2020, 93, 1008-1012.
G. Härter et al., “COVID-19 in people living with human immunodeficiency virus: a case series of 33 patients”, Infection 2020, 48, 681-686.
J. Dhamija, T. Choudhury, P. Kumar and A. Tetarbe, “Classification of swine flu patients using gene expression: For a safer future.” In Proceedings of the International Conference on Communication, Computing and Internet of Things, Chennai, India, 15-17 February 2018.
N. Shaltout et. al.,” Comparing PCA to information gain as a feature selection method for Influenza-A classification”, In Proceedings of the International Conference on Intelligent Informatics and Biomedical Sciences, Okinawa, Japan, 28-30 November 2015.
X. Qian et. al., “M3 Lung-Sys: A deep learning system for multi-class lung pneumonia screening from CT imaging” IEEE Journal Biomedical Health Information, 2020, 24, 3539-3550.
J. Pei, et al. “Improving prediction accuracy of influenza-like illnesses in hospital emergency departments”, In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, Shanghai, China, 18-21 December 2013.
E. Elbasi, “Reliable abnormal event detection from IoT surveillance systems”, In Proceedings of the 7th International Conference on the Internet of Things: Systems, Management and Security, Paris, France, 14-16 December 2020.
E. Elbasi, D. K. Ayanoglu and A. I. Zreikat, “Determination of patient’s hearing sensitivity using data mining techniques”, In Proceedings of the IEEE 12th International Conference on Application of Information and Communication Technologies, Almaty, Kazakhstan, 17-19 October 2018.
E. Elbasi, S. Mathew, A. Topcu and W. Abdelbaki, “A survey on machine learning and Internet of Things for COVID-19”, In Proceedings of the World AI IoT Congress, Seattle, USA, 10-13 May 2021.
E. Elbasi et al.,” Crop Prediction Model Using Machine Learning Algorithms”, Applied Sciences, 2023, 13, 9288.
A. E. Topcu et al.,” Social Media Zero-Day Attack Detection Using TensorFlow”, Electronics 2023, 12, 3554.
A. E. Topcu et al., “Text Analysis of Smart Cities: A Big Data-based Model”, International Journal of Intelligent Systems and Applications in Engineering, vol 11, issue 4, 724-733, 2023.
B. Yildiz, “Reinforcement learning using fully connected, attention, and transformer models in knapsack problem solving”, Concurrency and Computation: Practice and Experience, vol. 34, issue 9, 2022.
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