Differences in ischemic heart disease between males and females using predictive artificial intelligence models DOI Open Access
Muayyad Ahmad, Salam Bani Hani

Electronic Journal of General Medicine, Journal Year: 2024, Volume and Issue: 21(5), P. em607 - em607

Published: Sept. 19, 2024

<b>Background: </b>Cardiovascular health and preventative strategies are influenced by the sex of individuals. To forecast cardiac events or detect ischemic heart disease (IHD) early, machine-learning algorithms can analyze complex patient data patterns. Early detection allows for lifestyle changes, medication management, invasive treatments to slow progression improve outcomes.<br /> <b>Aim</b>: compare predict differences in primary sources IHD burden between males females various age groups, geographical regions, death versus alive, comorbidity levels.<br <b>Methods: </b>A predictive retrospective design was implemented this study. Electronic records were extracted, which equally distributed among with IHD. The dataset consisted patients who admitted 2015 2022. Two eight models generated Modeler software study: Bayesian network model, achieved highest area under curve score (0.600), Chi-squared automatic interaction (CHAID) overall accuracy (57.199%).<br <b>Results: </b>The study sample included 17,878 men women, 58% whom had no comorbidities 1.7% died. Age, Charlson index score, location all predicted IHD, but more influential. analysis showed that odds 40-59 60-79, mortality risk 80-100. North south Jordan higher rates middle-aged from north middle governorates according CHAID.<br <b>Conclusion: </b>By using artificial intelligence, clinicians outcomes, treatment quality, save lives fight against cardiovascular illnesses. patterns outcomes.

Language: Английский

Differences in ischemic heart disease between males and females using predictive artificial intelligence models DOI Open Access
Muayyad Ahmad, Salam Bani Hani

Electronic Journal of General Medicine, Journal Year: 2024, Volume and Issue: 21(5), P. em607 - em607

Published: Sept. 19, 2024

<b>Background: </b>Cardiovascular health and preventative strategies are influenced by the sex of individuals. To forecast cardiac events or detect ischemic heart disease (IHD) early, machine-learning algorithms can analyze complex patient data patterns. Early detection allows for lifestyle changes, medication management, invasive treatments to slow progression improve outcomes.<br /> <b>Aim</b>: compare predict differences in primary sources IHD burden between males females various age groups, geographical regions, death versus alive, comorbidity levels.<br <b>Methods: </b>A predictive retrospective design was implemented this study. Electronic records were extracted, which equally distributed among with IHD. The dataset consisted patients who admitted 2015 2022. Two eight models generated Modeler software study: Bayesian network model, achieved highest area under curve score (0.600), Chi-squared automatic interaction (CHAID) overall accuracy (57.199%).<br <b>Results: </b>The study sample included 17,878 men women, 58% whom had no comorbidities 1.7% died. Age, Charlson index score, location all predicted IHD, but more influential. analysis showed that odds 40-59 60-79, mortality risk 80-100. North south Jordan higher rates middle-aged from north middle governorates according CHAID.<br <b>Conclusion: </b>By using artificial intelligence, clinicians outcomes, treatment quality, save lives fight against cardiovascular illnesses. patterns outcomes.

Language: Английский

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