Open AccessHybrid Classification Method for Dengue Prediction
Author(s) -
Prashansa Taneja,
Nisha Gautam
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f7892.088619
Subject(s) - computer science , support vector machine , data mining , artificial intelligence , machine learning , dengue fever , classifier (uml) , majority rule , precision and recall , python (programming language) , raw data , preprocessor , pattern recognition (psychology) , immunology , biology , programming language , operating system
Data mining is defined as the process in which useful information is extracted from the raw data. In order to acquire essential knowledge it is essential to extract large amount of data.. In this existing work, the technique of SVM is applied for the prediction of dengue. The SVM classifier has less accuracy and high execution time for the prediction. To improve the accuracy of prediction the voting based classification approach will be applied for the dengue prediction. The proposed method will be implemented in python and results will be analyzed in terms of accuracy, precision, recall and execution time.