A Novel Data Mining Approach for Detection of Polio Disease Using Spatio-Temporal Analysis

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.