Analysis of COVID-19 and its Impact on Alzheimer’s Patient using Machine Learning Techniques


  • R. Sivakani
  • M. Syed Masood



Alzheimer disease, COVID-19, classification, clustering, naïve Bayes, random forest


In this world, there is fast growth in technology, as technology growth is there the human also move fast based on the growth in technology. New diseases also growing fast in the world.  In this paper, a semi-supervised approach has been proposed for the classification of the COVID-19 and a study has been done to analyze the impact of the covid on Alzheimer’s disease patients. Coronavirus disease is a respiratory infection disease and Alzheimer’s disease is a brain disease. From the literature, it has been analyzed that, because of the coronavirus the immunity system will be affected in humans and there is a chance to affect the brain also. Classification and clustering have been done on the coronavirus dataset and validated using a 10-fold validation process. The classifiers applied are Naïve Bayes and Random Forest; the results obtained are 99.88% and 100% accuracy. Also, the clustering has been applied and 2 clusters are generated for grouping the classes. Then a study has been done for predicting the impact of the coronavirus on Alzheimer’s patients.


Coronavirus History. [Online]. Available at:

Mayo Clinic. [Online]. Available at:

Coronavirus disease (COVID-19). [Online]. Available at:

Q. Xie, X. He, F. Yang, X. Liu, Y. Li, Y. Liu, Z.-M. Yang, J. Yu, B. Zhang, and W. Zhao, “Analysis of the genome sequence and prediction of b-cell epitopes of the envelope protein of middle east respiratory syndrome-coronavirus,” IEEE Transactions on Computational Biology and Bioinformatics, vol. 15, no. 4, pp. 1344-1350, 2018.

J. Fuk-Woo Chan, S. Yuan and K.-H. Kok, “A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster,” Lancet, vol. 395, pp. 514–523, 2020.

K.-Y. Hwa, W. M. Lin, Y.-I. Hou and T.-M. Yeh, “Molecular mimicry between SARS coronavirus spike protein and human protein,” Frontiers in the Convergence of Bioscience and Information Technologies, 2007, pp. 294-298.

Q. Li, X. Guan, P. Wu, X. Wang, L. Zhou and Y. Tong, “Review of early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia,” The new England Journal of Medicine, vol. 382, pp. 1199-1207, 2020.

M. E. Killerby, H. M. Biggs, C. M. Midgley, S. I. Gerber and J. T. Watson, “Middle East respiratory syndrome coronavirus transmission,” Emerging Infectious Diseases, vol. 26, pp. 191-198, 2020.

R. Lu, X. Zhao, J. Li and P. Niu, “Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding,” The Lancet, vol. 395, pp. 565-574, 2020.

X. Weng and S. Neethirajan, “Immunosensor based on antibody-functionalized MoS2 for rapid detection of avian coronavirus on cotton thread,” IEEE Sensors Journal, vol. 18, pp. 4358–4363, 2018.

I. Ahn and J.-H. Jang, “Comparative study of Middle East respiratory syndrome coronavirus using bioinformatics techniques,” IEEE International Conference on Bioinforrnatics and Biomedicine, 2015. pp. 1718-1719.

A. Kumar, A. Bhattacharya and A. Kashyap, “Antigenic Epitope prediction of small envelope protein and designing a vaccine by using reverse vaccinology approach against SARS Coronavirus Tor2 strain,” Proceedings of the International Conference on Advanced Computing Technologies (ICACT), 2013, pp. 1-6.

Y. Li, H. Li, R. Fan, B. Wen and J. Zhang, “Coronavirus infections in the central nervous system and respiratory tract show distinct features in hospitalized children,” Intervirology, vol. 59, pp. 163–169, 2016.

R. S. Murray, B. Brown, D. Brian and G. F. Cabirac, “Detection of coronavirus RNA and antigen in multiple sclerosis brain,” The American Neurological Association, vol. 31, pp. 525–533, 1992.

G. A. de Erausquin, H. Snyder, M. Carrillo, A. A. Hosseini and T. S. Brugha, S. Seshadri, “The chronic neuropsychiatric sequelae of COVID-19: The need for a prospective study of viral impact on brain functioning,” Alzheimer’s & Dementia – The Journal of the Alzheimer’s Association, vol. 17, pp. 1056-1065, 2020.

M. Bostanciklioğlu, “Severe acute respiratory syndrome coronavirus 2 is penetrating to dementia research,” Bentham Science Publishers, vol. 17, pp. 42–343, 2020.

Y.-F. Chang, J. C. Huang, L.-C. Su, Y.-M. A. Chen, C.-C. Chen and C. Chou, “Localized surface plasmon coupled fluorescence fiber-optic biosensor for severe acute respiratory syndrome coronavirus nucleocapsid protein detection,” Biosensors and Bioelectronics, vol. 25, pp. 320–325, 2009.

Y. Hsu et al., “Investigation of the binding affinity of C-terminal domain of SARS coronavirus nucleocapsid protein to nucleotide using AlGaN/GaN high electron mobility transistors,” Sensors and Actuators B: Chemical, vol. 193, pp. 334–339, 2012.

R. Sivakani and G. A. Ansari, “Imputation using machine learning technique,” Proceedings of the IEEE International Conference on Computer, Communication and Signal Processing, 2020, pp. 1-6.

R. Sivakani and G. A. Ansari, “Machine learning framework for implementing Alzheimer’s disease,” Proceedings of the IEEE International Conference on Communication and Signal Processing, 2020, pp. 0588-0592.

J. Hasel, E. Mathieu, D. Beltekian, B. Macdonald, C. Giattino, E. Ortiz-Ospina, M. Roser and H. Ritchie, “A cross-country database of COVID-19 testing,” Scientific Data, vol. 7, Article number 345, 2020.

I. Arpaci, S. Huang, M. Al-Emran, M. N. Al-Kabi, M. Peng, “Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms,” Multimedia Tools and Applications, Springer, vol. 80, pp. 11943–11957, 2021.

V. J. Sharmila and D. Jemi Florinabel, “Deep learning algorithm for COVID-19 classification using chest X-Ray images,” Computational and Mathematical Methods in Medicine, Hindawi, vol. 2021, Article ID 9269173, 2021.

Coronavirus (COVID-19): Tips for Dementia Caregivers. [Online]. Available at:

The coronavirus pandemic is killing people with diabetes or Alzheimer’s who didn't even contract the virus. [Online]. Available at: Accessed on February 12, 2021.

E. E. Brown, S. Kumar, T. K. Rajji, B. G. Pollock and B. H. Mulsant, “Anticipating and mitigating the impact of the COVID-19 pandemic on Alzheimer’s disease and related dementias,” The American Journal of Geriatric Psychiatry, Elsevier, vol. 28, pp. 712–721, 2020.

J. Li, X. Long, H. Huang, J. Tang, C. Zhu, S. Hu, J. Wu, J. Li, Z. Lin and N. Xiong, “Resilience of Alzheimer’s disease to COVID-19,” Journal of Alzheimer’s Disease, IOS Press, vol. 77, pp. 67–73, 2020.

Q. Q. Wang, P. B. Davis, M. E. Gurney and R. Xu, “COVID-19 and dementia: Analyses of risk, disparity and outcomes from electronic health records in the US. Alzheimer’s & Dementia,” The Alzheimer’s Association, Wiley Publication, vol. 17, pp. 1297–1306, 2021.

G. Abate, M. Memo and D. Uberti, “Impact of COVID-19 on Alzheimer’s disease risk: Viewpoint for research action,” Healthcare, MDPI, vol. 8, pp. 1-10, 2020.




How to Cite

Sivakani, R., & Syed Masood, M. (2022). Analysis of COVID-19 and its Impact on Alzheimer’s Patient using Machine Learning Techniques. International Journal of Computing, 21(4), 468-474.