Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach
This addresses heart attack risk prediction for vulnerable populations post-COVID-19, but it is incremental as it applies existing methods to new data with specific insights.
This study tackled the problem of identifying heart attack risk in individuals over 40 post-COVID-19 by analyzing 13 key risk factors using a hybrid machine learning approach on a unique dataset, revealing strong associations and aggravated risk for postmenopausal patients.
The COVID-19 pandemic has significantly increased the incidence of post-infection cardiovascular events, particularly myocardial infarction, in individuals over 40. While the underlying mechanisms remain elusive, this study employs a hybrid machine learning approach to analyze epidemiological data in assessing 13 key heart attack risk factors and their susceptibility. Based on a unique dataset that combines demographic, biochemical, ECG, and thallium stress-tests, this study categorizes distinct subpopulations against varying risk profiles and then divides the population into 'at-risk' (AR) and 'not-at-risk' (NAR) groups using clustering algorithms. The study reveals strong association between the likelihood of experiencing a heart attack on the 13 risk factors studied. The aggravated risk for postmenopausal patients indicates compromised individual risk factors due to estrogen depletion that may be, further compromised by extraneous stress impacts, like anxiety and fear, aspects that have traditionally eluded data modeling predictions.