Key result
Machine learning models achieve 95% accuracy in predicting AMI probability among workers.
Why the study?
Cardiovascular diseases are the leading cause of mortality in Latin America, particularly acute myocardial infarction, creating a need to predict AMI probability in the working-age population.
Can machine learning models accurately predict the probability of acute myocardial infarction in a working-age population based on atherogenic, paraclinical, and anthropometric variables?
Cross-Sectional (n=202)
No
Can machine learning models accurately predict the probability of acute myocardial infarction in a working-age population based on atherogenic, paraclinical, and anthropometric variables?
Machine learning models using clinical and anthropometric data can predict the probability of acute myocardial infarction with high accuracy (>90%) in a working-age population.
ML models may aid AMI risk prediction in workers; leaves open clinical utility pending prospective validation.
Cardiovascular diseases are the leading cause of mortality in Latin America, particularly acute myocardial infarction (AMI), which is the primary cause of atherosclerotic cardiovascular morbidity. This study aims to develop a predictive model for the probability of AMI occurrence in the working-age population, based on atherogenic indices, paraclinical variables, and anthropometric measures. The research conducted a cross-sectional study involving 427 workers aged 40 years or older in Popayán, Colombia. Out of this population, 202 individuals were screened with a 95% confidence interval and a 5% error margin. Epidemiological, anthropometric, and paraclinical data were collected. A binary logistic regression model was employed to identify variables directly associated with the probability of AMI. Predictive classification models were generated using statistical software JASP and the programming language Python. During the training stage, JASP produced a model with an accuracy of 87.5%, while Python generated a model with an accuracy of 90.2%. In the validation stage, JASP achieved an accuracy of 93%, and Python reached 95%. These results establish an effective model for predicting the probability of AMI in the working population.
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Urbano-Cano et al. (2023) conducted a cross-sectional in Acute myocardial infarction (n=202). Machine learning predictive models was evaluated on Model accuracy for predicting AMI probability. Machine learning predictive models using Python and JASP achieved validation accuracies of 95% and 93%, respectively, for predicting acute myocardial infarction probability in workers.
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