A Novel Data Mining Approach for Detection of Polio Disease Using Spatiooral Analysis

Suleman Khan; Farooque Azam; Muhammad Waseem Anwar; Yawar Rasheed; Mudassar Saleem; Nouman Ejaz

doi:10.1145/3404555.3404591

A Novel Data Mining Approach for Detection of Polio Disease Using Spatiooral Analysis

Suleman Khan
, Farooque Azam
, Muhammad Waseem Anwar
, Yawar Rasheed
, Mudassar Saleem
, Nouman Ejaz

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

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 spatiooral 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.

Original language	English
Title of host publication	ICCAI 2020
Subtitle of host publication	Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence
Place of Publication	New York, NY
Publisher	Association for Computing Machinery (ACM)
Pages	223-227
Number of pages	5
ISBN (Electronic)	978-1-4503-7708-9
DOIs	https://doi.org/10.1145/3404555.3404591
Publication status	Published - 23 Apr 2020
Externally published	Yes
Event	6th International Conference on Computing and Artificial Intelligence, ICCAI 2020 - Tiangong University, Virtual, Tianjin, China Duration: 23 Apr 2020 → 26 Apr 2020 Conference number: 6 https://www.iconf.org/conference/iccai2020

Publication series

Name	Proceedings of the International Conference on Computing and Artificial Intelligence
Publisher	ACM
Volume	2020

Conference

Conference	6th International Conference on Computing and Artificial Intelligence, ICCAI 2020
Abbreviated title	ICCAI 2020
Country/Territory	China
City	Tianjin
Period	23/04/20 → 26/04/20
Internet address	https://www.iconf.org/conference/iccai2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

n/a OA procedure
geospatial
polio virus detection
Spatiooral
symptomatic treatment
eruption

Access to Document

10.1145/3404555.3404591

Cite this

Khan, S., Azam, F., Anwar, M. W., Rasheed, Y., Saleem, M., & Ejaz, N. (2020). A Novel Data Mining Approach for Detection of Polio Disease Using Spatiooral Analysis. In ICCAI 2020: Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence (pp. 223-227). (Proceedings of the International Conference on Computing and Artificial Intelligence; Vol. 2020). Association for Computing Machinery (ACM). https://doi.org/10.1145/3404555.3404591

Khan, Suleman ; Azam, Farooque ; Anwar, Muhammad Waseem et al. / A Novel Data Mining Approach for Detection of Polio Disease Using Spatiooral Analysis. ICCAI 2020: Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence. New York, NY : Association for Computing Machinery (ACM), 2020. pp. 223-227 (Proceedings of the International Conference on Computing and Artificial Intelligence).

@inproceedings{bf7f592759cd47fdba07b4098731e09c,

title = "A Novel Data Mining Approach for Detection of Polio Disease Using Spatiooral Analysis",

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 spatiooral 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.",

keywords = "n/a OA procedure, geospatial, polio virus detection, Spatiooral, symptomatic treatment, eruption",

author = "Suleman Khan and Farooque Azam and Anwar, \{Muhammad Waseem\} and Yawar Rasheed and Mudassar Saleem and Nouman Ejaz",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 6th International Conference on Computing and Artificial Intelligence, ICCAI 2020, ICCAI 2020 ; Conference date: 23-04-2020 Through 26-04-2020",

year = "2020",

month = apr,

day = "23",

doi = "10.1145/3404555.3404591",

language = "English",

series = "Proceedings of the International Conference on Computing and Artificial Intelligence",

publisher = "Association for Computing Machinery (ACM)",

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}

Khan, S, Azam, F, Anwar, MW, Rasheed, Y, Saleem, M & Ejaz, N 2020, A Novel Data Mining Approach for Detection of Polio Disease Using Spatiooral Analysis. in ICCAI 2020: Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence. Proceedings of the International Conference on Computing and Artificial Intelligence, vol. 2020, Association for Computing Machinery (ACM), New York, NY, pp. 223-227, 6th International Conference on Computing and Artificial Intelligence, ICCAI 2020, Tianjin, China, 23/04/20. https://doi.org/10.1145/3404555.3404591

A Novel Data Mining Approach for Detection of Polio Disease Using Spatiooral Analysis. / Khan, Suleman; Azam, Farooque; Anwar, Muhammad Waseem et al.
ICCAI 2020: Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence. New York, NY: Association for Computing Machinery (ACM), 2020. p. 223-227 (Proceedings of the International Conference on Computing and Artificial Intelligence; Vol. 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - A Novel Data Mining Approach for Detection of Polio Disease Using Spatiooral Analysis

AU - Khan, Suleman

AU - Azam, Farooque

AU - Anwar, Muhammad Waseem

AU - Rasheed, Yawar

AU - Saleem, Mudassar

AU - Ejaz, Nouman

N1 - Conference code: 6

PY - 2020/4/23

Y1 - 2020/4/23

N2 - 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 spatiooral 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.

AB - 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 spatiooral 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.

KW - n/a OA procedure

KW - geospatial

KW - polio virus detection

KW - Spatiooral

KW - symptomatic treatment

KW - eruption

UR - https://www.scopus.com/pages/publications/85092220518

U2 - 10.1145/3404555.3404591

DO - 10.1145/3404555.3404591

M3 - Conference contribution

AN - SCOPUS:85092220518

T3 - Proceedings of the International Conference on Computing and Artificial Intelligence

SP - 223

EP - 227

BT - ICCAI 2020

PB - Association for Computing Machinery (ACM)

CY - New York, NY

T2 - 6th International Conference on Computing and Artificial Intelligence, ICCAI 2020

Y2 - 23 April 2020 through 26 April 2020

ER -

Khan S, Azam F, Anwar MW, Rasheed Y, Saleem M, Ejaz N. A Novel Data Mining Approach for Detection of Polio Disease Using Spatiooral Analysis. In ICCAI 2020: Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence. New York, NY: Association for Computing Machinery (ACM). 2020. p. 223-227. (Proceedings of the International Conference on Computing and Artificial Intelligence). doi: 10.1145/3404555.3404591

A Novel Data Mining Approach for Detection of Polio Disease Using Spatiooral Analysis

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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