Dengue Outbreak Prediction using Regression Model in Chittoor District, Andhra Pradesh, India
J. Avanija1, G. Sunitha2, R. Hitesh Sai Vittal3
1Dr. J. Avanija, Department of CSE, Sree Vidyanikethan Engineering College, Tirupati, India.
2Dr. G. Sunitha*, Department of CSE, Sree Vidyanikethan Engineering College, Tirupati, India.
3R. Hitesh Sai Vittal, Department of CSE, Sree Vidyanikethan Engineering College, Tirupati, India. 

Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 10057-10060 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9519118419/2019©BEIESP | DOI: 10.35940/ijrte.D9519.118419

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Abstract: Dengue fever is one of the tropical diseases, also called as break bone fever. It is caused by transmission of dengue virus through bite from Aedes mosquitoes. Dengue fever is previously predicted regionally using gradient descent algorithm. Annually 50 million to 528 million people get affected by dengue fever and 10,000 to 20,000 die. Reason behind this paper is to predict outbreak of dengue fever regionally by using features such as temperature, rainfall, humidity because breeding of Aedes mosquitoes is related to these features. Bagging and boosting techniques are used with gradient descent to predict more accurately the occurrence of dengue in a region. Data is collected from regional government weather office. Data is pre-processed like filling missing values and normalizing values. Feature selection processes like dimensionality reduction is done on pre-processed dataset. Gradient descent is applied with bagging and boosting. Accuracy is calculated by plotting graphs and by calculating Mean Standard Deviation (MSD) and Mean Absolute Error (MAE). By prediction of dengue fever before its occurrence in a region, makes it easy to vaccinate people in that region and dengue can be controlled and stopped from becoming an outbreak.
Keywords: Epidemic Outbreak, Data Analysis, Prediction Modeling, Deep Learning.
Scope of the Article: Deep Learning.