Internet technology has affected humans' lives in many disciplines. The search engine is one of the most important Internet tools in that it allows people to search for what they want. Search queries entered in a web search engine can be used to predict dengue incidence. This vector borne disease causes severe illness and kills a large number of people every year. This dissertation utilizes the capabilities of search queries related to dengue and climate to forecast the number of dengue cases. Several machine learning techniques are applied for data analysis, including Multiple Linear Regression, Artificial Neural Networks, and the Seasonal Autoregressive Integrated Moving Average. Predictive models produced from these machine learning methods are measured for their performance to find which technique generates the best model for dengue prediction. The results of experiments presented in this dissertation indicate that search query data related to dengue and climate can be used to forecast the number of dengue cases. The performance measurement of predictive models shows that Artificial Neural Networks outperform the others. These results will help public health officials in planning to deal with the outbreaks.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc500167 |
| Date | 05 1900 |
| Creators | Chartree, Jedsada |
| Contributors | Mikler, Armin, Buckles, Bill P., 1942-, Huang, Yan, Mikler, Susie, Caragea, Cornelia |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | xii, 116 pages : illustrations (chiefly color), Text |
| Rights | Public, Chartree, Jedsada, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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