| Issue |
ITM Web Conf.
Volume 57, 2023
Fifth International Conference on Advances in Electrical and Computer Technologies 2023 (ICAECT 2023)
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 11 | |
| Section | Software Engineering & Information Technology | |
| DOI | https://doi.org/10.1051/itmconf/20235701004 | |
| Published online | 10 November 2023 | |
Machine Learning Techniques for Disease Prediction
1 School of Computer Science and Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune, INDIA
2 School of Computer Science and Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune, INDIA
* Nikhil Potnis: nikhilshilpa29@gmail.com
Epidemic disease prediction is a critical area of research that has garnered increasing attention in recent years, particularly in the wake of the COVID-19 pandemic. One promising avenue for predicting the spread of diseases is through the analysis of social media data, such as Twitter. Machine learning (ML) techniques can be applied to Twitter data to identify patterns and trends that may be indicative of an emerging epidemic. For example, natural language processing (NLP) techniques can be used to analyze the language used in tweets to identify keywords and phrases that are commonly associated with a particular disease. Additionally, sentiment analysis can be used to assess the overall mood of the Twitter community, which can be a useful predictor of disease outbreaks. By combining these techniques with real-world data on disease incidence and other relevant factors, it may be possible to develop highly accurate models for predicting the spread of epidemic diseases, which could have important implications for public health policy and emergency response planning.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.