Hybrid Classification Method for Dengue Prediction
Prashansa Taneja1, Nisha Gautam2
1Prashansa Taneja (Research Scholar, CSE, AP Goyal Shimla University, Shimla, India.
2Nisha Gautam (HOD – CSE, AP Goyal Shimla University, Shimla, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1858-1861 | Volume-8 Issue-6, August 2019. | Retrieval Number: F7892088619/2019©BEIESP | DOI: 10.35940/ijeat.F7892.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Dengue Prediction, Hybrid Classifier, SVM