Dengue incidence prediction models are very important at present as the dengue cases becoming a major health issue in tropical and subtropical countries. Dengue fever is one of the major health related issues as reported in World Health Organization (WHO). In order to curb this problem, it is important for the government to create a predictive system so that precaution steps could be taken. This study builds a dengue incidence prediction model to avoid epidemic using climate models in real time. Data mining techniques such as clustering and multiple regression are used to model the data in order to get the best fitting regression curve. In the next step, a real time adaptive computation software will be developed that could predict the dengue incidences immediately.
|Title of host publication||2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)|
|Number of pages||6|
|Publication status||Published - 6 Apr 2017|
- Dengue Incidences
- Machine Learning
- K-means Cluster
- Multiple Regression