• Swarada Deshpande
• Poonam Doddamani
• Aishwarya Dixit
• Preet Dalsania

In today’s world, millions of cases of dengue are reported ever year. The number of cases has increased, afflicting many individuals. For prediction of dengue clinical methodology comprises of antigens and anti-bodies tests. The tests are conducted on the blood samples collected from the patients. In our proposed system we are stratifying dengue into Dengue Fever (DF), Dengue Hemorrhagic Fever (DHF) and healthy patients. The dataset [GDS5093] being referred in this proposed model are of acute dengue patients. Existing work uses PSO approach which achieved the accuracy of 90.91%, in order to achieve high accuracy we are using optimization algorithms like Spider Monkey Optimization (SMO) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and to increase the optimality of the model, we have also used Probabilistic Neural Network (PNN). PNN uses feedforward technique for classification.

SMO, NSGA-II, PNN, DF, DHF.

"Stratification of Dengue Fever Using SMO and NSGA-II Optimization Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 5, page no.926-929, MAY-2018, Available :http://www.jetir.org/papers/JETIR1805573.pdf

"Stratification of Dengue Fever Using SMO and NSGA-II Optimization Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 5, page no. pp926-929, MAY-2018, Available at : http://www.jetir.org/papers/JETIR1805573.pdf