Towards prediction of cerebrospinal meningitis disease occurrence using logistics regression – a web based application

Cerebrospinal meningitis (CSM) is characterized by acute severe infection of the central nervous system causing inflammation of the meninges with associated morbidity and mortality. The information about its symptoms, time and season of spread, most affected region, its fatality rate, type and how easily it causes major disabilities in patients can be modelled and utilized in its treatment, and prevention. This research uses data mining techniques to predict the occurrence of CSM in terms of those liable to be infected by the disease using feature information about the region and the patient. It encompasses data collection, preprocessing, exploration, algorithm training, prediction, and web hosting. The intention is to help in managing the resources needed for both treatment and prevention. The outcome of the research indicated that the proposed technique is viable for the task, considering the number of correct predictions that was reported when the application was deployed and tested.