# Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach

arXiv: https://arxiv.org/abs/2505.21139 · HTML: https://mlsanity.com/paper/2505.21139/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 51 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 1 of 1)

- [VIRDOCD: a VIRtual DOCtor to Predict Dengue Fatality](https://mlsanity.com/paper/2104.14282/) (2021)

## Abstract (arXiv)

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.