Published 16-09-2024
Keywords
- Environmental Factors,
- Machine Learning
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Abstract
The rapid spread of infectious diseases presents a significant global health challenge, emphasizing the need for accurate forecasting of disease outbreaks and epidemics. Machine learning (ML) has emerged as a promising tool for predicting disease patterns based on various data sources. This paper explores the development and application of ML approaches for predicting disease outbreaks and epidemics, utilizing epidemiological data and environmental factors. The primary objective is to enhance early intervention and containment efforts, ultimately reducing the impact of infectious diseases on public health. The paper discusses key methodologies, challenges, and future directions in this field, highlighting the potential of ML to revolutionize disease surveillance and response strategies.
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References
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