Prediction of the Fatal Acute Complications of Myocardial Infarction via Machine Learning Algorithms
-
Reza Ghafari
,
Amir Sorayaie Azar
,
Ali Ghafari
,
Fatemeh Moradabadi Aghdam
,
Morteza Valizadeh
,
Naser Khalili
,
Shima Hatamkhani
Abstract
Background: Myocardial infarction (MI) is a major cause of death, particularly during the first year. The avoidance of potentially fatal outcomes requires expeditious preventative steps. Machine learning (ML) is a subfield of artificial intelligence science that detects the underlying patterns of available big data for modeling them. This study aimed to establish an ML model with numerous features to predict the fatal complications of MI during the first 72 hours of hospital admission.
Methods: We applied an MI complications database that contains the demographic and clinical records of patients during the 3 days of admission based on 2 output classes: dead due to the known complications of MI and alive. We utilized the recursive feature elimination (RFE) method to apply feature selection. Thus, after applying this method, we reduced the number of features to 50. The performance of 4 common ML classifier algorithms, namely logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost), was evaluated using 8 classification metrics (sensitivity, specificity, precision, false-positive rate, false-negative rate, accuracy, F1-score, and AUC).
Results: In this study of 1699 patients with confirmed MI, 15.94% experienced fatal complications, and the rest remained alive. The XGBoost model achieved more desirable results based on the accuracy and F1-score metrics and distinguished patients with fatal complications from surviving ones (AUC=78.65%, sensitivity=94.35%, accuracy=91.47%, and F1-score=95.14%). Cardiogenic shock was the most significant feature influencing the prediction of the XGBoost algorithm.
Conclusion: XGBoost algorithms can be a promising model for predicting fatal complications following MI.
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28. Amsterdam EA, Wenger NK, Brindis RG, Casey DE Jr, Ganiats TG, Holmes DR Jr, Jaffe AS, Jneid H, Kelly RF, Kontos MC, Levine GN, Liebson PR, Mukherjee D, Peterson ED, Sabatine MS, Smalling RW, Zieman SJ; ACC/AHA Task Force Members; Society for Cardiovascular Angiography and Interventions and the Society of Thoracic Surgeons. 2014 AHA/ACC guideline for the management of patients with non-ST-elevation acute coronary syndromes: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014;130:2354-2394.
2. Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, et al. Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association. Circulation 2021;143:e254-e743.
3. Kutty RS, Jones N, Moorjani N. Mechanical complications of acute myocardial infarction. Cardiol Clin. 2013;31:519-531, vii-viii.
4. Montrief T, Davis WT, Koyfman A, Long B. Mechanical, inflammatory, and embolic complications of myocardial infarction: An emergency medicine review. Am J Emerg Med 2019;37:1175-1183.
5. Cao Y, Fang X, Ottosson J, Näslund E, Stenberg E. A Comparative Study of Machine Learning Algorithms in Predicting Severe Complications after Bariatric Surgery. J Clin Med 2019;8:668.
6. Munna MTA, MM Alam, Allayear SM, K Sarker, Ara SJF. Prediction Model for Prevalence of Type-2 Diabetes Complications with ANN Approach Combining with K-Fold Cross Validation and K-Means Clustering. In: Arai K, Bhatia R, eds. Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 69. Cham: Springer; 2020. p. 1031–1045.
7. Mehta VS, O"brien H, Elliott MK, Sidhu BS, Gould J, Razavi R, Niederer S, Rinaldi CA. Machine learning based major complication prediction for patients undergoing transvenous lead extraction. EP Europace 2021;23(Supplement_3):euab116.511
8. Bhavsar KA, Abugabah A, Singla J, AlZubi AA, Bashir AK, et al. A comprehensive review on medical diagnosis using machine learning. Computers, Materials & Continua 2021;67:1997–2014.
9. Sorayaie Azar A, Ghafari A, Ostadi M, Babaei Rikan S, Ghafari R, Farajpouri M, Sheikhzadeh P. Covidense: Providing a suitable solution for diagnosing Covid-19 lung infection based on deep learning from chest X-ray images of patients. Frontiers in Biomedical Technologies 2021;8:131-142.
10. Than MP, Pickering JW, Sandoval Y, Shah ASV, Tsanas A, Apple FS, Blankenberg S, Cullen L, Mueller C, Neumann JT, Twerenbold R, Westermann D, Beshiri A, Mills NL; MI3 Collaborative. Machine Learning to Predict the Likelihood of Acute Myocardial Infarction. Circulation 2019;140:899-909.
11. Baloglu UB, Talo M, Yildirim O, Tan RS, Acharya UR. Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recognition Letters 2019;122:23-30.
12. Golovenkin SE, Gorban A, Mirkes E, Shulman VA, Rossiev DA, Shesternya PA, et al. Myocardial infarction complications Database. University of Leicester. Dataset 2020. https://doi.org/10.25392/leicester.data.12045261.v3 (01 August 2022).
13. Golovenkin SE, Gorban AN, Mirkes EM, Shulman VA, Rossiev DA, Shesternya PA, et al. Complications of myocardial infarction: a database for testing recognition and prediction systems. https://www.stat.rice.edu/~scottdw/stat541/PROJECTS/uci-data/MI-Complications/MI-desc.pdf. (01 August 2022)
14. Golovenkin SE, Bac J, Chervov A, Mirkes EM, Orlova YV, Barillot E, Gorban AN, Zinovyev A. Trajectories, bifurcations, and pseudo-time in large clinical datasets: applications to myocardial infarction and diabetes data. Gigascience 2020;9:giaa128.
15. Darst BF, Malecki KC, Engelman CD. Using recursive feature elimination in random forest to account for correlated variables in high dimensional data. BMC Genet 2018;19(Suppl 1):65.
16. Parikh R, Mathai A, Parikh S, Chandra Sekhar G, Thomas R. Understanding and using sensitivity, specificity and predictive values. Indian J Ophthalmol 2008;56:45-50.
17. Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol 2019;20:e262-e273.
18. MAM Hasan, Shin J, Das U, Yakin Srizon A. Identifying Prognostic Features for Predicting Heart Failure by Using Machine Learning Algorithm. ICBET '21: Proceedings of the 2021 11th International Conference on Biomedical Engineering and Technology March 2021; p. 40-46.
19. Zhang N, Yang G, Gao Z, Xu C, Zhang Y, Shi R, Keegan J, Xu L, Zhang H, Fan Z, Firmin D. Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI. Radiology 2019;291:606-617.
20. Mansoor H, Elgendy IY, Segal R, Bavry AA, Bian J. Risk prediction model for in-hospital mortality in women with ST-elevation myocardial infarction: A machine learning approach. Heart Lung 2017;46:405-411.
21. Mullasari AS, Balaji P, Khando T. Managing complications in acute myocardial infarction. J Assoc Physicians India. 2011;59 Suppl:43-48.
22. Chen YH, Huang SS, Lin SJ. TIMI and GRACE Risk Scores Predict Both Short-Term and Long-Term Outcomes in Chinese Patients with Acute Myocardial Infarction. Acta Cardiol Sin 2018;34:4-12.
23. Thomas KE, Josephson ME. The role of electrophysiology study in risk stratification of sudden cardiac death. Prog Cardiovasc Dis 2008;51:97-105.
24. Samsky MD, Morrow DA, Proudfoot AG, Hochman JS, Thiele H, Rao SV. Cardiogenic Shock After Acute Myocardial Infarction: A Review. JAMA 2021;326:1840-1850.
25. Harjola VP, Lassus J, Sionis A, Køber L, Tarvasmäki T, Spinar J, Parissis J, Banaszewski M, Silva-Cardoso J, Carubelli V, Di Somma S, Tolppanen H, Zeymer U, Thiele H, Nieminen MS, Mebazaa A; CardShock Study Investigators; GREAT network. Clinical picture and risk prediction of short-term mortality in cardiogenic shock. Eur J Heart Fail 2015;17:501-509.
26. Webb JG, Sleeper LA, Buller CE, Boland J, Palazzo A, Buller E, White HD, Hochman JS. Implications of the timing of onset of cardiogenic shock after acute myocardial infarction: a report from the SHOCK Trial Registry. SHould we emergently revascularize Occluded Coronaries for cardiogenic shocK? J Am Coll Cardiol 2000;36(3 Suppl A):1084-1090.
27. Collet JP, Thiele H, Barbato E, Barthélémy O, Bauersachs J, Bhatt DL, Dendale P, Dorobantu M, Edvardsen T, Folliguet T, Gale CP, Gilard M, Jobs A, Jüni P, Lambrinou E, Lewis BS, Mehilli J, Meliga E, Merkely B, Mueller C, Roffi M, Rutten FH, Sibbing D, Siontis GCM; ESC Scientific Document Group. 2020 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. Eur Heart J 2021;42:1289-1367.
28. Amsterdam EA, Wenger NK, Brindis RG, Casey DE Jr, Ganiats TG, Holmes DR Jr, Jaffe AS, Jneid H, Kelly RF, Kontos MC, Levine GN, Liebson PR, Mukherjee D, Peterson ED, Sabatine MS, Smalling RW, Zieman SJ; ACC/AHA Task Force Members; Society for Cardiovascular Angiography and Interventions and the Society of Thoracic Surgeons. 2014 AHA/ACC guideline for the management of patients with non-ST-elevation acute coronary syndromes: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014;130:2354-2394.
| Files | ||
| Issue | Vol 18 No 4 (2023): J Teh Univ Heart Ctr | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/jthc.v18i4.14827 | |
| Keywords | ||
| Artificial intelligence Machine learning Myocardial infarction Prognosis Mortality | ||
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How to Cite
1.
Ghafari R, Sorayaie Azar A, Ghafari A, Moradabadi Aghdam F, Valizadeh M, Khalili N, Hatamkhani S. Prediction of the Fatal Acute Complications of Myocardial Infarction via Machine Learning Algorithms. J Tehran Heart Cent. 2023;18(4):278-287.
