Clinical Dengue Data Analysis and Prediction using Multiple Classifiers: An Ensemble Techniques (Global Journal of Computer Science and Technology, 2022)
The dengue infection is caused by the mosquito Aedes aegypti According to WHO 50 to 100 million dengue infections will occur every year Data-miming techniques will extract information from the raw data Dengue symptoms are fever severe headache body pain vomiting diarrhea cough pain in abdomen etc The research work is carried out on real data and the patient data is collected from the Department of General Medicine PESIMSR Kuppam Andrapradesh Dataset consists of 18 attributes and one target value Research work has been done on binary classification to classify dengue positive DF and dengue negative NDF cases using different ML techniques The proposed work demonstrates that ensemble techniques bagging boosting and stacking gives better results than other models The Extreme Gradient Boost XGB Random Forest by majority voting and stacking with different meta classifiers are the ensemble techniques used for the binary classification The dataset is divided into 80 training and 20 testing dataset Performance parameters used for the analysis are accuracy precision recall and f1 score and compared the proposed model with other ML models The experimental results shows that the accuracy of extended boost random forest and stacking is 98 99 99 for training dataset and 97 94 98 testing dataset respectively The extended metrics ROC Precision -Recall curve and AUC better analysis
Keywords: dengue fever; aedes aegypti; XGB; stacking; ROC; AUC