Bibliographic Details
| Title: |
Prediction of dengue patients using deep learning methods amid complex weather conditions in Jaipur, India. |
| Authors: |
Dhaked, Dheeraj Kumar1 (AUTHOR) ddhakar9@gmail.com, Sharma, Omveer2 (AUTHOR) osharma@campus.haifa.ac.il, Gopal, Yatindra3 (AUTHOR) hellogpal4u@gmail.com, Gopal, Ram4 (AUTHOR) r.gopal1996@gmail.com |
| Source: |
Discover Public Health. 2/15/2025, Vol. 22 Issue 1, p1-13. 13p. |
| Subject Terms: |
*PUBLIC health infrastructure, *RISK assessment, *DATA analysis, *LONG short-term memory, *DENGUE, *CONVOLUTIONAL neural networks, *DESCRIPTIVE statistics, *BAROCLINICITY, *DEEP learning, *WEATHER, *ARTIFICIAL neural networks, *EPIDEMICS, *STATISTICS, *MACHINE learning, *COMPARATIVE studies, *ALGORITHMS, *DISEASE incidence, *DISEASE risk factors |
| Geographic Terms: |
INDIA |
| Abstract: |
Dengue outbreaks pose an escalating challenge, especially in northern India, where both affected regions and incidence rates have expanded. Developing a reliable model for accurate dengue incidence forecasting remains a daunting task. This study rigorously evaluates advanced deep learning algorithms, including artificial neural networks (ANN), convolutional neural networks (CNN), and long short-term memory networks (LSTM), to improve predictive accuracy for Jaipur city region. The system integrates a robust recommendation engine, employing CNN to interpret monthly dengue surveillance and meteorological data from 2015 to 2019 in Jaipur, Rajasthan, India. This integration creates a sophisticated fusion, not just a collection of tools, effectively bridging the gap between physical and virtual realms. The result is an interactive, immersive, and more accurate patient prediction, facilitating health infrastructure management. Validation of the proposed one-dimensional CNN (1DCNN) model demonstrates high accuracy and robustness in predicting dengue cases, supported by various performance metrics. This study represents the first comprehensive evaluation of diverse algorithms for dengue incidence prediction, offering the potential to accurately monitor dengue dynamics and optimize health infrastructure management in the region. [ABSTRACT FROM AUTHOR] |
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| Database: |
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