Mortality among older adults Jordanians with coronary heart disease: Intelligent algorithms prediction DOI Open Access
Salam Bani Hani, Muayyad Ahmad

Electronic Journal of General Medicine, Journal Year: 2025, Volume and Issue: 22(1), P. em626 - em626

Published: Jan. 13, 2025

<b>Background and aim: </b>Worldwide, coronary heart disease (CHD) is the main cause of death. To prevent save lives, this study uses a machine learning algorithm (MLA), subfield artificial intelligence, to predict death vs. life outcomes among older persons with CHD.<br /> <b>Methods: </b>Large-scale data was retrieved from electronic health records 3,331 elderly patients congestive failure retrospectively. Information gathered on population in Jordan who were hospitalized public hospitals between 2015 2021.<br <b>Results: </b>Based accuracy level (91.4%) area under curve (71.7%) eight prediction models created, Chi-square automatic interaction detector chosen versus adults CHD. The sequence algorithms began medical diagnosis, location, age, pulse pressure.<br <b>Conclusion: </b>Attempts should be made use expertise many specialists clinical screening patient databases speed up diagnosis process MLAs, which are thought useful tool for identifying CHD at high risk dying.

Language: Английский

Mortality among older adults Jordanians with coronary heart disease: Intelligent algorithms prediction DOI Open Access
Salam Bani Hani, Muayyad Ahmad

Electronic Journal of General Medicine, Journal Year: 2025, Volume and Issue: 22(1), P. em626 - em626

Published: Jan. 13, 2025

<b>Background and aim: </b>Worldwide, coronary heart disease (CHD) is the main cause of death. To prevent save lives, this study uses a machine learning algorithm (MLA), subfield artificial intelligence, to predict death vs. life outcomes among older persons with CHD.<br /> <b>Methods: </b>Large-scale data was retrieved from electronic health records 3,331 elderly patients congestive failure retrospectively. Information gathered on population in Jordan who were hospitalized public hospitals between 2015 2021.<br <b>Results: </b>Based accuracy level (91.4%) area under curve (71.7%) eight prediction models created, Chi-square automatic interaction detector chosen versus adults CHD. The sequence algorithms began medical diagnosis, location, age, pulse pressure.<br <b>Conclusion: </b>Attempts should be made use expertise many specialists clinical screening patient databases speed up diagnosis process MLAs, which are thought useful tool for identifying CHD at high risk dying.

Language: Английский

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