Prediction of Epidemic Outbreaks in Specific Region using Machine Learning
A.K. Bhavana1, Chalumuru Suresh2, B. V. Kiranmayee3, Kadem Shravan Kumar4
1AK.Bhavana*,Department of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad, India.
2Chalumuru Suresh, Department of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad, India.
3B.V.Kiranmayee, Department of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad, India.
4Kadem Shravan Kumar, Department of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad, India.
Manuscript received on January 13, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 1536-1547 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1697029420/2020©BEIESP | DOI: 10.35940/ijitee.D1697.029420
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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: Dengue is one amongst the common infectious disease which is caused by dengue virus and transmitted to humans by mosquitoes with this many are infected in varied regions around the world per year. The reason for this virus is atmospheric conditions, which plays a vital role in the outbreak of dengue. Therefore early prediction of dengue is the key to regulate outbreak and reduces the transmission within the community. To overcome this we are using various machine learning (ML) algorithms such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest tree (RF) and Decision Tree (DT) are used to predict the dengue outbreak. Prediction is done based on weather parameters like monthly wise maximum temperature, minimum temperature, average temperature, mean temperature, humidity and Precipitation which is considered as weather dataset and this weather dataset is pre- processed using label encoding function before applying into the training models. The performances of all the models are calculated based on weather dataset. After considering performance of all the models we choose random forest as a best predictor for dengue outbreak.
Keywords: ANN, Decision Tree, Machine Learning, Random Forest tree, SVM and Weather Parameters
Scope of the Article: Machine Learning