Full Text (PDF)
Abstract
Background and aim: Worldwide, coronary heart disease (CHD) is the main cause of death. To prevent heart disease and save lives, this study uses a machine learning algorithm (MLA), a subfield of artificial intelligence, to predict death vs. life outcomes among older persons with CHD.
Methods: Large-scale data was retrieved from the electronic health records of 3,331 elderly patients with congestive heart failure retrospectively. Information was gathered on the population in Jordan who were hospitalized in public health hospitals between 2015 and 2021.
Results: Based on the accuracy level (91.4%) and area under the curve (71.7%) of the eight prediction models created, the Chi-square automatic interaction detector algorithm was chosen to predict death versus life among older adults with CHD. The sequence of death prediction algorithms began with the medical diagnosis, location, age, and pulse pressure.
Conclusion: Attempts should be made to use the expertise of many specialists and clinical screening data gathered from patient databases to speed up the diagnosis process with MLAs, which are thought to be a useful tool for identifying CHD patients who are at high risk of dying.
License
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Original Article
ELECTRON J GEN MED, Volume 22, Issue 1, February 2025, Article No: em626
https://doi.org/10.29333/ejgm/15854
Publication date: 13 Jan 2025
Article Views: 80
Article Downloads: 73
Open Access References How to cite this article