Predicting Heart Disease Through the Application of Machine Learning Techniques Using the Python
- DOI
- 10.2991/978-94-6463-370-2_68How to use a DOI?
- Keywords
- Artificial intelligence; heart disease; convenience
- Abstract
This paper is mainly about using machine learning methods to predict cardiovascular disease. Because cardiovascular disease now ranks first in mortality and the methods and costs of curing heart disease are very small and expensive, so why not take a step earlier? As for discovering diseases, there are not many projects that use machines to predict diseases, and it costs a lot of money to predict diseases in hospitals. Using machines can be faster and more accurate. Another point is that society has entered the era of artificial intelligence, and the medical industry It should also usher in an improvement and should keep pace with other industries, so I want people to experience predicting diseases, such as performing cardio-tracheal examinations faster and more accurately with less money. This helps a lot of families save a lot of money and get the same services as a hospital, which is not a good thing and that’s why I did this research.
- 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.
Cite this article
TY - CONF AU - Zihang Fu PY - 2024 DA - 2024/02/14 TI - Predicting Heart Disease Through the Application of Machine Learning Techniques Using the Python BT - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023) PB - Atlantis Press SP - 666 EP - 676 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-370-2_68 DO - 10.2991/978-94-6463-370-2_68 ID - Fu2024 ER -