A Comparative Study of Disease Prediction for Different Population Size and Time Constraints

DOI

10.2991/978-94-6463-417-4_49How to use a DOI?

Keywords

Data prediction; Deep learning; Disease Prediction; Classification

Abstract

In recent years, the rapid advancement of the internet has propelled humanity into an era characterized by an exponential increase in the volume of information. Decision-making in a variety of economic and social fields has been significantly impacted by the development of unstructured data by big data. Data prediction is now the primary use of big data, driven by advancements in deep-learning neural networks. Examples include typical disease forecasts based on electronic health data, influenza forecasts, traffic forecasts, and more. Therefore, based on the reading and analysis of relevant literature in the past three years, this paper categorizes the development of data prediction into three areas. One is classification based on research questions, another is classification based on research methodology, and the third aspect is classification rooted in measurement methods.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Dingfei Guo
AU  - Wei Li
PY  - 2024
DA  - 2024/05/07
TI  - A Comparative Study of Disease Prediction for Different Population Size and Time Constraints
BT  - Proceedings of the 2024 5th International Conference on Big Data and Informatization Education (ICBDIE 2024)
PB  - Atlantis Press
SP  - 532
EP  - 538
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-417-4_49
DO  - 10.2991/978-94-6463-417-4_49
ID  - Guo2024
ER  -