Clinical Dengue Data Analysis and Prediction using Multiple Classifiers: An Ensemble Techniques

Dengue infection is caused by the mosquito Aedes aegypti. According to WHO, 50 100 million dengue infections will occur every year. Data-miming techniques extract information from raw data. symptoms are fever, severe headache, body pain, vomiting, diarrhoea, cough, pain in abdomen, etc. The research work carried out on real data and patient collected Department of General Medicine, PESIMSR, Kuppam, Andrapradesh. Dataset consists 18 attributes one target value. Research has been done a binary classification classify positive (DF) negative (NDF) cases using different ML techniques. proposed demonstrates that ensemble bagging, boosting, stacking give better results than other models. Extreme Gradient Boost (XGB), Random Forest majority voting, with meta-classifiers used for classification. dataset divided into 80% training 20 % testing dataset. Performance parameters analysis accuracy, precision, recall, f1 score, compared model