Machine Learning na Predição do Infarto Agudo do Miocárdio: Uma Revisão Sistemática de Literatura
Resumo
As doenças cardiovasculares (DCVs) estão entre as principais causas de morte no mundo, com o infarto agudo do miocárdio (IAM) entre os eventos mais fatais. Técnicas de Machine Learning (ML) têm potencial para apoiar a predição e o diagnóstico precoce. Este estudo apresenta uma Revisão Sistemática da Literatura baseada em [Kitchenham and Charters 2007] sobre aplicações de ML na predição de IAM. As buscas nas bases IEEE e PubMed identificaram 28 estudos primários. O XGBoost apresentou o melhor desempenho (AUC 0,87–0,99; acurácia até 98%). Os resultados destacam o potencial do ML no apoio ao diagnóstico de IAM, embora avanços em transparência e equidade ainda sejam necessários para uma adoção clínica responsável.Referências
Baidya, S. and Gupta, V. (2024). Predicting cardiac complications of myocardial infarction patients using machine learning. In 2024 IEEE International Conference on Big Data (BigData), pages 7454–7462, Washington, DC, USA. IEEE.
Bodini, M., Rivolta, M. W., and Sassi, R. (2020). Interpretability analysis of machine learning algorithms in the detection of st-elevation myocardial infarction. Computing in Cardiology, pages 1–4.
Chen, Z., Zhang, L., Li, R., Wang, J., Chen, L., Jin, Y., Gao, M., Han, Z., Zhang, K., Wang, J., Li, X., and Yang, C. (2024). The development and validation of a prognostic prediction modeling study in acute myocardial infarction patients after percutaneous coronary intervention: hemorrhea and major cardiovascular adverse events. Journal of Thoracic Disease, 16(9):6216–6228.
Cui, H. and Han, Y. (2023). Addressing racial bias in cardiovascular disease risk prediction with fair data augmentation. In 2023 International Conference on Artificial Intelligence in Medicine (ICAIM), pages 89–97. IEEE.
Ehghaghi, M., Zhou, P.-Y., Cheng, W. Y., Rajabi, S., Kuo, C.-H., and Lee, E.-S. A. (2023). Interpretable disease prediction from clinical text by leveraging pattern disentanglement. In 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), pages 1–7. IEEE.
Forhad, S., Mim, T., Mosarat, Z., Taluckder, M. S., Al Masum, A., Islam, M. T., and Shuvo, M. R. K. (2025). Heart attack prediction: A comparative analysis of supervised machine learning algorithms. In 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN), pages 1–7. IEEE.
Guo, C. et al. (2025a). Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction. BMC Cardiovascular Disorders, 25(1):362.
Guo, L., Wu, Y., Ma, N., and An, Y. (2025b). Kgd-gnn: A knowledge-guided graph neural network for myocardial infarction localization via 12-lead ecg. In 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 1–5.
Kim, M. et al. (2024). Acute myocardial infarction prognosis prediction with reliable and interpretable artificial intelligence system. JAMIA Open, 7(2):ooae061.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE-2007-01, Keele University & Durham University.
Kucukakcali, Z., Akbulut, U. O., Basaga, S. F., and Akar, N. (2025). Beyond black boxes: Interpretable ai with explainable neural networks (enns) for acute myocardial infarction using hematological parameters. Computers in Biology and Medicine, 169:107659.
Li, L., Ren, W., Lei, Y., Xu, L., and Ning, X. (2025). Hybrid cnn-transformer-woa model with xgboost-shap feature selection for arrhythmia risk prediction. BMC Medical Informatics and Decision Making, 25(1):291.
Lin, Q. et al. (2025). Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model. Frontiers in Cardiovascular Medicine, 12:1444323.
Mekonnen, D. et al. (2025). Artificial intelligence-assisted left ventricular global longitudinal strain assessment in ami patients. International Journal of Cardiovascular Imaging, 41(6):1225–1236.
Mesinovic, M., Watkinson, P., and Zhu, T. (2025). Explainable machine learning for predicting icu mortality in myocardial infarction patients using pseudo-dynamic data. Scientific Reports, 15:27887.
Raja, G. et al. (2024). Cardionetfusion: A deep learning and explainable ai integrated system for cardiovascular disease detection. In 2024 IEEE International Conference on Data Mining Workshops (ICDMW), pages 1–6.
Salman, M. S., Shila, N., Hasan, K., Ahmed, P., Keya, M., Khushbu, S., and Noori, S. (2021). Data mining technique for prediction system of heart disease using associative classifications. pages 1–5.
Sammani, A. et al. (2021). Automatic multilabel detection of icd10 codes in dutch cardiology discharge letters using neural networks. NPJ Digital Medicine, 4:32.
Shen, C. et al. (2025). Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in ami with cardiogenic shock. BMC Cardiovascular Disorders, 25(1):197.
Singh, A., Alapatt, B. P., and George, J. P. (2025). An explainable ai-driven deep learning algorithm for heart disease detection in healthcare. 2025 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), pages 471–477.
Takci, H. (2018). Improvement of heart attack prediction by the feature selection methods. Turkish Journal of Electrical Engineering and Computer Sciences, 26(1):1–10.
Tang, Z. et al. (2025). Identification of endoplasmic reticulum stress-related genes in acute myocardial infarction. Biochemical Genetics.
Wang, C. et al. (2022). Predicting the prognosis of patients in the coronary care unit: A multi-category machine learning model using xgboost. Frontiers in Cardiovascular Medicine, 9:890067.
Wang, Y. et al. (2024). Evaluating the effectiveness of feature selection and explainable ai in predicting acute myocardial infarction. In 2024 5th International Conference on Machine Learning and Computer Application (ICMLCA), pages 6–10.
World Health Organization (2023). Cardiovascular diseases (cvds).
Yang, R. et al. (2025). Risk factors and interpretability tool of in-hospital mortality in critically ill ami patients. Journal of Intensive Medicine, 4(2):103–112.
Yu, Q., Fu, M., Hou, Z., and Wang, Z. (2025). Elucidating predictors of preoperative acute heart failure in older people with hip fractures through machine learning and shap analysis: a retrospective cohort study. BMC Geriatrics, 25(1):268.
Zhang, G. et al. (2023). Atherosclerotic plaque vulnerability quantification system. Frontiers in Cardiovascular Medicine, 10:1245698.
Zhu, Y. et al. (2025). Interpretable hybrid-ensemble acute myocardial infarction diagnosis with clinical transparency. International Journal of Cardiology, 438:133598.
Bodini, M., Rivolta, M. W., and Sassi, R. (2020). Interpretability analysis of machine learning algorithms in the detection of st-elevation myocardial infarction. Computing in Cardiology, pages 1–4.
Chen, Z., Zhang, L., Li, R., Wang, J., Chen, L., Jin, Y., Gao, M., Han, Z., Zhang, K., Wang, J., Li, X., and Yang, C. (2024). The development and validation of a prognostic prediction modeling study in acute myocardial infarction patients after percutaneous coronary intervention: hemorrhea and major cardiovascular adverse events. Journal of Thoracic Disease, 16(9):6216–6228.
Cui, H. and Han, Y. (2023). Addressing racial bias in cardiovascular disease risk prediction with fair data augmentation. In 2023 International Conference on Artificial Intelligence in Medicine (ICAIM), pages 89–97. IEEE.
Ehghaghi, M., Zhou, P.-Y., Cheng, W. Y., Rajabi, S., Kuo, C.-H., and Lee, E.-S. A. (2023). Interpretable disease prediction from clinical text by leveraging pattern disentanglement. In 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), pages 1–7. IEEE.
Forhad, S., Mim, T., Mosarat, Z., Taluckder, M. S., Al Masum, A., Islam, M. T., and Shuvo, M. R. K. (2025). Heart attack prediction: A comparative analysis of supervised machine learning algorithms. In 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN), pages 1–7. IEEE.
Guo, C. et al. (2025a). Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction. BMC Cardiovascular Disorders, 25(1):362.
Guo, L., Wu, Y., Ma, N., and An, Y. (2025b). Kgd-gnn: A knowledge-guided graph neural network for myocardial infarction localization via 12-lead ecg. In 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 1–5.
Kim, M. et al. (2024). Acute myocardial infarction prognosis prediction with reliable and interpretable artificial intelligence system. JAMIA Open, 7(2):ooae061.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE-2007-01, Keele University & Durham University.
Kucukakcali, Z., Akbulut, U. O., Basaga, S. F., and Akar, N. (2025). Beyond black boxes: Interpretable ai with explainable neural networks (enns) for acute myocardial infarction using hematological parameters. Computers in Biology and Medicine, 169:107659.
Li, L., Ren, W., Lei, Y., Xu, L., and Ning, X. (2025). Hybrid cnn-transformer-woa model with xgboost-shap feature selection for arrhythmia risk prediction. BMC Medical Informatics and Decision Making, 25(1):291.
Lin, Q. et al. (2025). Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model. Frontiers in Cardiovascular Medicine, 12:1444323.
Mekonnen, D. et al. (2025). Artificial intelligence-assisted left ventricular global longitudinal strain assessment in ami patients. International Journal of Cardiovascular Imaging, 41(6):1225–1236.
Mesinovic, M., Watkinson, P., and Zhu, T. (2025). Explainable machine learning for predicting icu mortality in myocardial infarction patients using pseudo-dynamic data. Scientific Reports, 15:27887.
Raja, G. et al. (2024). Cardionetfusion: A deep learning and explainable ai integrated system for cardiovascular disease detection. In 2024 IEEE International Conference on Data Mining Workshops (ICDMW), pages 1–6.
Salman, M. S., Shila, N., Hasan, K., Ahmed, P., Keya, M., Khushbu, S., and Noori, S. (2021). Data mining technique for prediction system of heart disease using associative classifications. pages 1–5.
Sammani, A. et al. (2021). Automatic multilabel detection of icd10 codes in dutch cardiology discharge letters using neural networks. NPJ Digital Medicine, 4:32.
Shen, C. et al. (2025). Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in ami with cardiogenic shock. BMC Cardiovascular Disorders, 25(1):197.
Singh, A., Alapatt, B. P., and George, J. P. (2025). An explainable ai-driven deep learning algorithm for heart disease detection in healthcare. 2025 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), pages 471–477.
Takci, H. (2018). Improvement of heart attack prediction by the feature selection methods. Turkish Journal of Electrical Engineering and Computer Sciences, 26(1):1–10.
Tang, Z. et al. (2025). Identification of endoplasmic reticulum stress-related genes in acute myocardial infarction. Biochemical Genetics.
Wang, C. et al. (2022). Predicting the prognosis of patients in the coronary care unit: A multi-category machine learning model using xgboost. Frontiers in Cardiovascular Medicine, 9:890067.
Wang, Y. et al. (2024). Evaluating the effectiveness of feature selection and explainable ai in predicting acute myocardial infarction. In 2024 5th International Conference on Machine Learning and Computer Application (ICMLCA), pages 6–10.
World Health Organization (2023). Cardiovascular diseases (cvds).
Yang, R. et al. (2025). Risk factors and interpretability tool of in-hospital mortality in critically ill ami patients. Journal of Intensive Medicine, 4(2):103–112.
Yu, Q., Fu, M., Hou, Z., and Wang, Z. (2025). Elucidating predictors of preoperative acute heart failure in older people with hip fractures through machine learning and shap analysis: a retrospective cohort study. BMC Geriatrics, 25(1):268.
Zhang, G. et al. (2023). Atherosclerotic plaque vulnerability quantification system. Frontiers in Cardiovascular Medicine, 10:1245698.
Zhu, Y. et al. (2025). Interpretable hybrid-ensemble acute myocardial infarction diagnosis with clinical transparency. International Journal of Cardiology, 438:133598.
Publicado
19/07/2026
Como Citar
PINTO, Evelly Victory Vieira; LUZ, Jose Wilker Pereira; SILVA, Luis Fernando Maia Santos.
Machine Learning na Predição do Infarto Agudo do Miocárdio: Uma Revisão Sistemática de Literatura. In: ENCONTRO NACIONAL DE COMPUTAÇÃO DOS INSTITUTOS FEDERAIS (ENCOMPIF), 13. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 89-96.
ISSN 2763-8766.
DOI: https://doi.org/10.5753/encompif.2026.21188.
