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AUTHOR(S):

Akinbo R. Y., Olatayo T. O., Lasisi T. A., Adejumo T. J., Titilola A. A

 

TITLE

Exploring Factors Associated with Heart Attack Risk Using Ordinal Logistic Regression

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ABSTRACT

Heart attack remains a major global public health concern, making the identification of key risk factors essential for effective prevention strategies. This study investigated factors associated with heart attack risk using ordinal logistic regression. Secondary data on 50,000 individuals were obtained from Kaggle.com and classified into three ordered risk levels: low, moderate, and high. Descriptive statistics, chi-square tests, and ordinal logistic regression were employed for analysis. The results showed that participants were predominantly middle-aged, with average body mass index, cholesterol level, and resting blood pressure falling within the overweight, borderline-high, and stage 1 hypertension ranges, respectively. Chi-square analyses indicated no significant association between heart attack risk and gender or chest pain type. Although the overall association between stress level and heart attack risk was not statistically significant, a significant linear trend suggested increasing risk with higher stress levels. Ordinal logistic regression further revealed that individuals with low stress levels were significantly more likely to belong to lower risk categories compared to those with high stress, while moderate stress showed no significant effect. Other conventional risk factors were not significant predictors. The model satisfied goodness-of-fit and proportional odds assumptions but exhibited low explanatory power. These findings highlight the importance of stress management in cardiovascular risk assessment and prevention.

KEYWORDS

Heart attack risk, Ordinal logistic regression, Stress level, Cardiovascular risk factors. Chi-square analysis

 

Cite this paper

Akinbo R. Y., Olatayo T. O., Lasisi T. A., Adejumo T. J., Titilola A. A. (2026) Exploring Factors Associated with Heart Attack Risk Using Ordinal Logistic Regression. International Journal of Medical Physiology, 8, 1-13

 

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