Identification of early myocardial infarction symptoms in adult male smokers using sparse attention mechanisms and quantile regression: results from 97,304 participants in a nationwide survey in Korea

Background: Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, with myocardial infarction (MI) being a significant contributor. This study aims to identify key demographic and behavioral factors influencing the early recognition of MI symptoms among adult male smokers and non-smokers. Methods: Utilizing Sparse Attention Mechanisms and comparing their performance with traditional models such as Classification and Regression Tree (CART), C4.5 and Rotation Random Forest, we analyze predictors of MI symptom recognition. Data from the 2021 Community Health Survey included 97,304 participants, with 30,858 male smokers and 66,446 male non-smokers. Results: The findings reveal that age, marital status, residential area, education level, high-risk drinking, diabetes prevalence and occupation significantly impact MI symptom recognition. Age was the most significant predictor, with older individuals showing higher recognition rates. Marital status and residential area were also important, indicating that married individuals and those living in cities or rural areas had higher recognition rates. Higher educational attainment was associated with better recognition, emphasizing the role of health literacy. High-risk drinking and diabetes prevalence showed a trend towards significance at higher quantiles, suggesting their impact on high-risk groups. Conclusions: The study highlights the need for targeted educational interventions focusing on high-risk groups such as older adults, those with lower educational attainment, and individuals with high-risk drinking behaviors or diabetes. Public health strategies should address regional disparities in healthcare access and involve spouses in educational programs to enhance MI symptom recognition.

Myocardial infarction; Symptom recognition; Sparse attention mechanisms; Multiple risk factors; Demographic factors

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