A Novel Data Mining Approach for Detection of Polio Disease Using Spatio-Temporal Analysis
Authors: 
Suleman Khan, Farooque Azam, Muhammad Waseem Anwar, Yawar Rasheed, Mudassar Saleem, Nouman EjazAuthors Info & Claims
Suleman Khan, Farooque Azam, Muhammad Waseem Anwar, Yawar Rasheed, Mudassar Saleem, Nouman EjazAuthors Info & ClaimsPages 223 - 227
Abstract
Polio is an epidemic disease, which may lead to paralysis and may be fatal enough to cause even death of the infected person. In most of the cases, polio virus has mild symptoms, so, there is a high probability that it can remain unnoticed. This paper aims to understand the eruption, severity and spread of polio virus from a spatio-temporal point of view. This research proposed a novel machine learning model to predict the chances of polio. Particularly, data sets are developed by getting data from several sources such as NIH (National Institute of Health), databases of medical stores and transport logs. Subsequently, K-mean algorithm is applied on the given data to predict the chances of polio's breakout. The preliminary study proved that the proposed model is significant step towards mitigating the challenges of this fatal disease. Furthermore, it also provides a platform/ framework, which can be extended in the development of an automated tool for polio virus detection.
References
[1]
S. Hussain, P. Boyle, P. Patel and R. Sullivan, "Eradicating polio in Pakistan: an analysis of the challenges and solutions to this security and health issue", Globalization and Health, vol. 12, no. 1, 2016.
[2]
Z. Bhutta, "The last mile in global poliomyelitis eradication", The Lancet, vol. 378, no. 9791, pp. 549--552, 2011.
[3]
B. Henry, "Canadian Pandemic Preparedness: Communications strategy", Canada Communicable Disease Report, vol. 44, no. 5, pp. 106--109, 2018.
[4]
I. Clinaero, "Polio Incubation Period", eMedTV: Health Information Brought to Life, 2019.
[5]
I. Blake et al., "Type 2 Poliovirus Detection after Global Withdrawal of Trivalent Oral Vaccine", New England Journal of Medicine, vol. 379, no. 9, pp. 834--845, 2018.
[6]
M. Famulare, C. Selinger, K. McCarthy, P. Eckhoff and G. Chabot-Couture, "Assessing the stability of polio eradication after the withdrawal of oral polio vaccine", PLOS Biology, vol. 16, no. 4, p. e2002468, 2018.
[7]
Fitchett, J., 2017. Why transition matters as much as eradication: lessons from global polio surveillance. International Health, 9(3), pp. 137--138. I. Clinaero, "Polio Incubation Period", eMedTV: Health Information Brought to Life, 2019.
[8]
"GPEI-The journey of a stool sample: Understanding polio surveillance", Polioeradication.org, 2019.
[9]
S. Lowther et al., "World Health Organization Regional Assessments of the Risks of Poliovirus Outbreaks", Risk Analysis, vol. 33, no. 4, pp. 664--679, 2013.
[10]
Ivanova et al., "Environmental Surveillance for Poliovirus and Other Enteroviruses: Long-Term Experience in Moscow, Russian Federation, 2004--2017", Viruses, vol. 11, no. 5, p. 424, 2019.
[11]
T. Cowger et al., "The role of supplementary environmental surveillance to complement acute flaccid paralysis surveillance for wild poliovirus in Pakistan - 2011-2013", PLOS ONE, vol. 12, no. 7, p. e0180608, 2017.
[12]
J. Patel et al., "Surveillance to Track Progress Toward Polio Eradication --- Worldwide, 2017--2018", MMWR. Morbidity and Mortality Weekly Report, vol. 68, no. 13, pp. 312--318, 2019.
[13]
A. Khan and M. Khan, "P2P inspired stochastic model for analysis of polio management in Pakistan", 2016 First IEEE International Conference on Computer Communication and the Internet (ICCCI), 2016.
[14]
I. Blake, P. Chenoweth, H. Okayasu, C. Donnelly, R. Aylward and N. Grassly, "Faster Detection of Poliomyelitis Outbreaks to Support Polio Eradication", Emerging Infectious Diseases, vol. 22, no. 3, pp. 449--456, 2016.
[15]
P. Olukanmi and B. Twala, "K-means-sharp: Modified centroid update for outlier-robust k-means clustering", 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech), 2017.
Index Terms
- A Novel Data Mining Approach for Detection of Polio Disease Using Spatio-Temporal Analysis
Recommendations
A Spatio-Temporal malware and country clustering algorithm: 2012 IIJ MITF case study
A huge number of botnet malware variants can be downloaded by zombie personal computers as secondary injections and upgrades according to their botmasters to perform different distributed/coordinated cyber attacks such as phishing, spam e-mail, ...
Malware detection using adaptive data compression
AISec '08: Proceedings of the 1st ACM workshop on Workshop on AISecA popular approach in current commercial anti-malware software detects malicious programs by searching in the code of programs for scan strings that are byte sequences indicative of malicious code. The scan strings, also known as the signatures of ...
A Survey on Malware Detection Using Data Mining Techniques
In the Internet age, malware (such as viruses, trojans, ransomware, and bots) has posed serious and evolving security threats to Internet users. To protect legitimate users from these threats, anti-malware software products from different companies, ...
Comments
Information & Contributors
Information
Published In
April 2020
563 pages
ISBN:9781450377089
DOI:10.1145/3404555
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- University of Tsukuba: University of Tsukuba
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 August 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICCAI '20
ICCAI '20: 2020 6th International Conference on Computing and Artificial Intelligence
April 23 - 26, 2020
Tianjin, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 50Total Downloads
- Downloads (Last 12 months)10
- Downloads (Last 6 weeks)1
Reflects downloads up to 21 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in