Gong FF, Vaitenas I, Malaisrie SC, Maganti K. Mechanical complications of acute myocardial infarction: a review. JAMA Cardiol. 2021;6(3):341–9.
Zhang Q, Wang L, Wang S, Cheng H, Xu L, Pei G, Wang Y, Fu C, Jiang Y, He C, et al. Signaling pathways and targeted therapy for myocardial infarction. Signal Transduct Target Ther. 2022;7(1):78.
Jenča D, Melenovskỳ V, Stehlik J, Staněk V, Kettner J, Kautzner J, Adámková V, Wohlfahrt P. Heart failure after myocardial infarction: incidence and predictors. ESC Heart Fail. 2021;8(1):222–37.
Zhang G, Si Y, Wang D, Yang W, Sun Y. Automated detection of myocardial infarction using a gramian angular field and principal component analysis network. IEEE Access. 2019;7:171570–83.
Sulthana AR, Jaithunbi A. Varying combination of feature extraction and modified support vector machines based prediction of myocardial infarction. Evol Syst. 2022;13(6):777–94.
Sinha N, Das A. Identification and localization of myocardial infarction based on analysis of ecg signal in cross spectral domain using boosted svm classifier. IEEE Trans Instrum Meas. 2021;70:1–9.
Anudeep R, Thangaraj SJJ. Accurate prediction of myocardial infarction by comparing logistic regression algorithm with catboost classifier. In: E3S Web of Conferences. 2023. 399 04019
Chen P, Wang B, Zhao L, Ma S, Wang Y, Zhu Y, Zeng X, Bai Z, Shi B. Machine learning for predicting intrahospital mortality in st-elevation myocardial infarction patients with type 2 diabetes mellitus. BMC Cardiovasc Disord. 2023;23(1):585.
Shetty MK, Kunal S, Girish M, Qamar A, Arora S, Hendrickson M, Mohanan PP, Gupta P, Ramakrishnan S, Yadav R, et al. Machine learning based model for risk prediction after st-elevation myocardial infarction: Insights from the north india st elevation myocardial infarction (norin-stemi) registry. Int J Cardiol. 2022;362:6–13.
Shimizu M, Suzuki M, Fujii H, Kimura S, Nishizaki M, Sasano T. Machine learning of microvolt-level 12-lead electrocardiogram can help distinguish takotsubo syndrome and acute anterior myocardial infarction. Cardiovasc Digit Health J. 2022;3(4):179–88.
Jafarian K, Vahdat V, Salehi S, Mobin M. Automating detection and localization of myocardial infarction using shallow and end-to-end deep neural networks. Appl Soft Comput. 2020;93:106383.
Zhang Z, Qiu H, Li W, Chen Y. A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction. BMC Med Inf Decis Mak. 2020;20:1–13.
Elmannai H, Saleh H, Algarni AD, Mashal I, Kwak KS, El-Sappagh S, Mostafa S. Diagnosis myocardial infarction based on stacking ensemble of convolutional neural network. Electronics. 2022;11(23):3976.
Chen Y, Shi J, Pu J. Development and validation of a random forest diagnostic model of acute myocardial infarction based on ferroptosis-related genes in circulating endothelial cells. Front Cardiovasc Med. 2021;8:663509.
Matter MA, Paneni F, Libby P, Frantz S, Stähli BE, Templin C, Mengozzi A, Wang Y-J, Kündig TM, Räber L, et al. Inflammation in acute myocardial infarction: the good, the bad and the ugly. Eur Heart J. 2024;45(2):89–103.
Wang M, Yao X, Chen Y. An imbalanced-data processing algorithm for the prediction of heart attack in stroke patients. IEEE Access. 2021;9:25394–404.
Olisah CC, Smith L, Smith M. Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective. Comput Methods Progr Biomed. 2022;220:106773.
Lin T-Y, Goyal P, Girshick R, He K, Dollár P. Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision; 2017. pp. 2980–2988
Centers for Disease Control and Prevention. 2022. Behavioral Risk Factor Surveillance System Survey Data. U.S. Department of Health and Human Services, https://www.cdc.gov/brfss.
Jurman G, Riccadonna S, Furlanello C. A comparison of mcc and cen error measures in multi-class prediction 2012
Rahman M, Zahin MM, Islam L. Effective prediction on heart disease: anticipating heart disease using data mining techniques. In: 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT); 2019. pp. 536–541 . IEEE
Rath A, Mishra D, Panda G, Pal M. Development and assessment of machine learning based heart disease detection using imbalanced heart sound signal. Biomed Signal Process Control. 2022;76:103730.
Khera R, Haimovich J, Hurley NC, McNamara R, Spertus JA, Desai N, Rumsfeld JS, Masoudi FA, Huang C, Normand S-L, et al. Use of machine learning models to predict death after acute myocardial infarction. JAMA Cardiol. 2021;6(6):633–41.
Ibrahim L, Mesinovic M, Yang K-W, Eid MA. Explainable prediction of acute myocardial infarction using machine learning and shapley values. IEEE Access. 2020;8:210410–7.
Bai Z, Hu S, Wang Y, Deng W, Gu N, Zhao R, Zhang W, Ma Y, Wang Z, Liu Z, et al. Development of a machine learning model to predict the risk of late cardiogenic shock in patients with st-segment elevation myocardial infarction. Ann Transl Med. 2021;9(14):11–2.
Sahu G, Ray KC. An efficient method for detection and localization of myocardial infarction. IEEE Trans Instrum Meas. 2021;71:1–12.
Huang R, Palmer SC, Cao Y, Zhang H, Sun Y, Su W, Liang L, Wang S, Wang Y, Xu Y, et al. Cardiac rehabilitation programs for chronic heart disease: a bayesian network meta-analysis. Can J Cardiol. 2021;37(1):162–71.
Mahesh T, Dhilip Kumar V, Vinoth Kumar V, Asghar J, Geman O, Arulkumaran G, Arun N. Adaboost ensemble methods using k-fold cross validation for survivability with the early detection of heart disease. Comput Intell and Neurosci. 2022;2022(1):9005278.
Hoque R, Billah M, Debnath A, Hossain SS, Sharif NB, et al. Heart disease prediction using svm. Int J Sci Res Arch. 2024;11(2):412–20.
Reddy LCS, Pasha SG, Bandela HB, Murthy K, Naidu UG, Shankar RS. Enhancing heart disease prediction with multiple imputation and feature selection in xgboost. In: 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT), 2024; pp. 419–424 . IEEE
Elshawi R, Al-Mallah MH, Sakr S. On the interpretability of machine learning-based model for predicting hypertension. BMC Med Inf Decis Mak. 2019;19:1–32.
Anand SS, Islam S, Rosengren A, Franzosi MG, Steyn K, Yusufali AH, Keltai M, Diaz R, Rangarajan S, Yusuf S. Risk factors for myocardial infarction in women and men: insights from the interheart study. Eur Heart J. 2008;29(7):932–40.
Sagris M, Antonopoulos AS, Theofilis P, Oikonomou E, Siasos G, Tsalamandris S, Antoniades C, Brilakis ES, Kaski JC, Tousoulis D. Risk factors profile of young and older patients with myocardial infarction. Cardiovasc Res. 2022;118(10):2281–92.
Park S, Kim D-W, Lee K, Park M-W, Chang K, Jeong MH, Ahn YK, Chae SC, Ahn TH, Rha SW, et al. Association between body mass index and three-year outcome of acute myocardial infarction. Sci Rep. 2024;14(1):365.
Zheng X, Yang Y, Chen J, Lu B. Dissecting the causal relationship between household income status and genetic susceptibility to cardiovascular-related diseases: insights from bidirectional mendelian randomization study. BMC Public Health. 2023;23(1):749.
McDermott M, Meah MN, Khaing P, Wang K-L, Ramsay J, Scott G, Rickman H, Burt T, McGowan I, Fairbairn T, et al. Rationale and design of scot-heart 2 trial: Ct angiography for the prevention of myocardial infarction. Cardiovasc Imaging. 2024;17(9):1101–12.
Mora T, Roche D, Rodríguez-Sánchez B. Predicting the onset of diabetes-related complications after a diabetes diagnosis with machine learning algorithms. Diabetes Res Clin Pract. 2023;204:110910.
Caldwell M, Martinez L, Foster JG, Sherling D, Hennekens CH. Prospects for the primary prevention of myocardial infarction and stroke. J Cardiovasc Pharmacol Ther. 2019;24(3):207–14.
Milazzo V, Cosentino N, Genovese S, Campodonico J, Mazza M, De Metrio M, Marenzi G. Diabetes mellitus and acute myocardial infarction: impact on short and long-term mortality. Diabetes Res Clin Pract. 2021;4:153–69.
留言 (0)