Abstract: Random forests have emerged as a versatile and highly accurate classification and regression methodology, requiring little tuning and providing interpretable outputs. Here, we briefly explore the possibility of applying this ensemble supervised machine learning technique to predict the vulnerability for complex disease - Dengue which is often baffled with chikungunya viral fever. This study presents a new-fangled approach to determine the significant prognosis factors in dengue patients. Random forests is used to visualize and determine the significant factors that can differentiate between the dengue patients and the healthy subjects and for constructing a dengue disease survivability prediction model during the boosting process to improve accuracy and stability and to reduce over fitting problems. The presented methodology may be incorporated in a variety of applications such as risk management, tailored health communication and decision support systems in healthcare

A. Shameem Fathima and D.Manimeglai. “Analysis of Significant Factors for Dengue Infection Prognosis Using the Random Forest Classifier”. International Journal of Advanced Computer Science and Applications (IJACSA) 6.2 (2015). http://dx.doi.org/10.14569/IJACSA.2015.060235

@article{Fathima2015,
title = {Analysis of Significant Factors for Dengue Infection Prognosis Using the Random Forest Classifier},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.060235},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060235},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {2},
author = {A. Shameem Fathima and D.Manimeglai}
}