Prediction of West Nile Virus using Ensemble Classifiers
R. Rishickesh1, A. Shahina2, A. Nayeemulla Khan3

1R. Rishickesh*, Department of Information Technology, SSN College of Engineering, Kalavakkam, India.
2A. Shahina, Department of Information Technology, SSN College of Engineering, Kalavakkam, India.
3A. Nayeemulla Khan, School of Computing Science and Engineering, VIT University, Chennai, India.
Manuscript received on September 12, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 3744-3749  | Volume-9 Issue-1, October 2019 | Retrieval Number: A9810109119/2019©BEIESP | DOI: 10.35940/ijeat.A9810.109119
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Abstract: West Nile Virus (WNV) is a disease caused by mosquitoes where human beings get infected by the mosquito’s bite. The disease is considered to be a serious threat to the society especially in the United States where it is frequently found in localities having water bodies. The traditional approach is to collect the traps of mosquitoes from a locality and check whether they are infected with virus. If there is a virus found then that locality is sprayed with pesticides. But this process is very time consuming and requires a lot of financial support. Machine learning methods can provide an efficient approach to predict the presence of virus in a locality using data related to the location and weather. This paper uses the dataset present in Kaggle which includes information related to the traps found in the locality and also about the information related to the locality’s weather. The dataset is found to be imbalanced hence Synthetic Minority Over sampling Technique (SMOTE), an upsampling method, is used to sample the dataset to balance it. Ensemble learning classifiers like random forest, gradient boosting and Extreme Gradient Boosting (XGB). The performance of ensemble classifiers is compared with the performance of the best supervised learning algorithm, SVM. Among the models, XGB gave the highest F-1 score of 92.93 by performing marginally better than random forest (92.78) and also SVM (91.16).
Keywords: West Nile Virus, Ensemble Learning Classifiers, Extreme Gradient Boosting, Synthetic Minority Over sampling Technique.