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GENE MARKERS IDENTIFICATION OF ACUTE MYOCARDIAL INFARCTION DISEASE BASED ON GENOMIC PROFILING THROUGH EXTREME GRADIENT BOOSTING (XGBoost)

*Rohmatul Fajriyah orcid scopus  -  Master Program in Statistics,, Indonesia
Havidzah Asri Isnandar  -  Undergraduate Program in Statistics, , Indonesia
Adhar Arifuddin  -  Master Program in Statistics, , Indonesia
Open Access Copyright (c) 2024 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
One disease that can cause death is Acute Myocardial Infarction (AMI). AMI, also known as a heart attack, is a condition that causes permanent damage to heart muscle tissue due to prolonged ischemia or lack of blood flow that occurs due to blockage of the epicardial coronary arteries and results in blood clots and limiting blood supply to the myocardium. During the years the young AMI patients are increasing. One of the ways to diagnose early is providing information of biomarkers related to this disease by implementing the bioinformatics data analysis. The research was conducted to look at the genomic profile of patients suffering from AMI based on without recurrent events and normal control, using the XGBoost method, due to its scalability and efficiency.  Based on the grid search of tuning hyperparameters, the XGBoost method gives a classification accuracy of 88.89%, AUC 90 and kappa 0.7805. These results indicate that the XGBoost method can classify patients suffering from AMI well. This research has identified three genes that contribute the most to classifying AMI patients, namely calponin 2, ribosomal protein S11 and myotropin. Based on the heatmap visualization, information was obtained that the three genes are class markers without recurrent events.

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Keywords: Bioinformatics; Microarray; XGBoost; Pre-processing; Filtering; heatmap

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  1. Ahammad, I., 2018, Identification of Key Proteins Associated with Myocardial Infarction using Bioinformatics and Systems Biology. 10.1101/308544
  2. Aji, A. K., Setiawan, A. A., Ariosta, A., and Pramudo, S.G., 2020, The Impact of Body Mass Index To Acute Myocardial Infarction in-Hospital Patients Mortality Rate in DR. KARIADI HOSPITAL, Jurnal Kedokteran Diponegoro (Diponegoro Medical Journal), vol. 9, no. 3, pp. 225-234, May. 2020. https://doi.org/10.14710/dmj.v9i3.27497
  3. Ando, H, Yamaji, K, Kohsaka, S., Ishii, H., Sakakura, K., Goto, R., Nkano, Y., Takashima, H., Ikari, Y., and Amano, T., 2022, Clinical Presentation and In-Hospital Outcomes of Acute Myocardial Infarction in Young Patients: Japanese Nationwide Registry. JACC: Asia. 2022 Oct, 2 (5) 574–585. https://doi.org/10.1016/j.jacasi.2022.03.013
  4. Anghel,L., Prisacariu,C., Sascău,R., Macovei,L., Cristea,E., Prisacariu, G., and Stătescu, C., 2019, Particularities of Acute Myocardial Infarction in Young Adults. Journal of Cardiovascular Emergencies,5(1) 25-31. https://doi.org/10.2478/jce-2019-0005
  5. Azab, A., & Elsayed, A., 2017, Acute Myocardial Infarction Risk Factors and Correlation of its Markers with Serum Lipids. Applied Biotechnology & Bioengineering, 3(4):00075
  6. Baans, O., Jambek, A., Said, K. A. M., 2019, Analysis of normalization method for DNA microarray data. Asia Pacific Journal of Molecular Biology and Biotechnology. 30-37. https://doi.org/10.35118/apjmbb.2019.027.4.04
  7. Chen, T., and Guestrin, C., 2016, XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794
  8. Cheng, M., An, S., and Li J., 2017, Identifying key genes associated with acute myocardial infarction. Medicine (Baltimore). 2017 Oct;96(42):e7741. doi: 10.1097/MD.0000000000007741.PMID:29049183;PMCID:PMC5662349
  9. Choudhuri, S., 2014, Bioinformatics for Beginners Genes, Genomes, Molecular Evolution, Databases and Analytical Tools. USA: Elsevier
  10. Chrissini, M.K. and Panagiotakos, D.B., 2022, Acute Myocardial Infarction in Young Patients and its Correlation with Obesity Status at Pre-adolescent Stage: A Narrative Review, The Open Cardiovascular Medicine Journal, 2022, Volume 16. http://dx.doi.org/10.2174/18741924-v16-e2206200
  11. Dimitrova, I.N., 2023, Acute Myocardial Infarction in Young Individuals: Demographic and Risk Factor Profile, Clinical Features, Angiographic Findings and In-Hospital Outcome. Cureus, 15(9): e45803. DOI 10.7759/cureus.45803
  12. Dozmorov, M., 2016, Filtering. Taken from https://mdozmorov.github.io/BIOS567/assets/presentation_Bioconductor/Filtering.pdf
  13. Doudesis D, Lee KK, Boeddinghaus J, Bularga A, Ferry AV, Tuck C, Lowry MTH, Lopez-Ayala P, Nestelberger T, Koechlin L, Bernabeu MO, Neubeck L, Anand A, Schulz K, Apple FS, Parsonage W, Greenslade JH, Cullen L, Pickering JW, Than MP, Gray A, Mueller C, Mills NL, 2023, CoDE-ACS Investigators. Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations. Nat Med. 2023 May;29(5):1201-1210. doi: 10.1038/s41591-023-02325-4.Epub2023May11.PMID:37169863;PMCID:PMC10202804
  14. Elgeldawi, A.S. E., 2021, Hyperparameter Tuning for Machine Learning Algorithms. Arabic Sentiment Analysis. Informatics, 8(4):49
  15. Fahmi, I., Nurachmah, E., Yona, S., and Herawati, T., 2023, Physical Activity and Associated Factors in Indonesian Patients with Acute Myocardial Infarction. Evidence Based Care, 12(4), 27-35. doi: 10.22038/ebcj.2022.66149.2734
  16. Fajriyah, R., 2021, Paper review: An overview of microarray technologies, BAMME, 1(1)
  17. Faresjö, Å., Karlsson, J.E., Segerberg, H., Lebena, A.m and Faresjö, T., 2023, Cardiovascular and psychosocial risks among patients below age 50 with acute myocardial infarction. BMC Cardiovasc Disord23, 121 (2023). https://doi.org/10.1186/s12872-023-03134-w
  18. Federico, A., Serra,A., Ha, M.K., Kohonen, P., Choi,J-S., Liampa, I., Nymark, P., Sanabria, N., Cattelani, L., Fratello, M., Kinaret, P.A.S., Jagiello, K., Puzyn, T., Melagraki, G., Gulumian, M., Afantitis, A., Sarimveis, H., Yoon, T-H., Grafström, R., and Greco, D., 2020, Transcriptomics in Toxicogenomics, Part II: Pre-processing and Differential Expression Analysis for High Quality Data. Nanomaterials, 10, 903. https://doi.org/10.3390/nano10050903
  19. Federico A, Saarimäki LA, Serra A, Del Giudice G, Kinaret PAS, Scala G, Greco D., 2022, Microarray Data Pre-processing: From Experimental Design to Differential Analysis. Methods Mol Biol., 2401:79-100. doi: https://doi.org/10.1007/978-1-0716-1839-4_7.PMID:34902124
  20. Fennich H, El Haddaji S, Oukerraj L, Zarzur J, Cherti M., 2019, Acute myocardial infarction among young adults under 40 years of age. Risk factors, clinical and angiographic characteristics. Cor Vasa, 61(6):578-583. doi: 10.33678/cor.2019.052
  21. Firani, N.K. and Prisilla, J. 2022. Procalcitonin and Troponin-I as Predictor of Mortality in Acute Myocardial Infarction Patients. INDONESIAN JOURNAL OF CLINICAL PATHOLOGY AND MEDICAL LABORATORY. 28, 2 (Jun. 2022), 170–174. DOI: https://doi.org/10.24293/ijcpml.v28i2.1817
  22. Gao, H., Wang, Y., Shen, A., Chen, H., and Li, H., 2021, Acute Myocardial Infarction in Young Men Under 50 Years of Age: Clinical Characteristics, Treatment, and Long-Term Prognosis. Int J Gen Med. 2021;14:9321-9331. https://doi.org/10.2147/IJGM.S334327
  23. Ghafari R, Azar AS, Ghafari A, Aghdam FM, Valizadeh M, Khalili N, Hatamkhani S., 2023, Prediction of the Fatal Acute Complications of Myocardial Infarction via Machine Learning Algorithms. J Tehran Heart Cent, 18(4):278-287. doi: 10.18502/jthc.v18i4.14827.PMID:38680646;PMCID:PMC11053239
  24. Gentleman, R., Carey,V., Huber, W., and Hahne, F., 2024, genefilter: methods for filtering genes from high-throughput experiments. R package version 1.86.0
  25. Gulati, R., Behfar, A., Narula, J., Kanwar,. A, Lerman, A., Cooper, L., Singh, M., 2020, Acute Myocardial Infarction in Young Individuals. Mayo Clin Proc. 2020 Jan;95(1):136-156. doi: 10.1016/j.mayocp.2019.05.001.PMID:31902409
  26. Hartopo, A.B., Susanti, V. Y., and Setianto B.Y., 2016, The Prevalence and Impact of Body Mass Index Category in Patients with Acute Myocardial Infarction, Acta Cardiologia Indonesiana, Vol 2 No. 2): 61-68
  27. Hu, Z., Liu, R., Hu, H., Ding, X., Ji, Y., Li, G., Wang, Y., Xie, S., Liu, X., Ding, Z., 2022, Potential biomarkers of acute myocardial infarction based on co expression network analysis. Experimental and Therapeutic Medicine, 23, 162. https://doi.org/10.3892/etm.2021.11085
  28. Idris, D. N. T., Taviyanda, D., and Mahanani, S., 2020, Characteristics of Acute Myocardial Infarction Patients. STRADA Jurnal Ilmiah Kesehatan, 9(2), 1017–1026. https://doi.org/10.30994/sjik.v9i2.414
  29. Islam, S.F.N.N., Sholahuddin, A., and Abdullah, A.S., 2021, Extreme Gradient Boosting (XGBoost) Method in Making Forecasting Application and Analysis of USD Exchange Rates Against Rupiah, Journal of Physics: Conference Series, vol. 1722, no. 1, p. 012016, 2021
  30. Jafary AH and Jafar TH, 2022, High Risk of Post-Myocardial Infarction Cardiac Arrest in Young Adults. JACC Asia. 2022 Oct 18;2(5):586-589. doi: 10.1016/j.jacasi.2022.06.008.PMID:36624796;PMCID:PMC9823283
  31. Jia HM, An FX, Zhang Y, Yan MZ, Zhou Y, Bian HJ., 2024, FASLG as a Key Member of Necroptosis Participats in Acute Myocardial Infarction by Regulating Immune Infiltration. Cardiol Res. 2024 Aug;15(4):262-274. doi: 10.14740/cr1652.Epub2024Jul30.PMID:39205966;PMCID:PMC11349138
  32. Jordan, M., and Mitchell, T., 2015, Machine learning: Trends, perspectives, and prospects. Science, pp. 255–260
  33. Juzar, D., Muzakkir, A., Ilhami, Y., Taufiq, N., Astiawati, T., R A, I. M., Pramudyo, M., Priyana, A., Hakim, A., Anjarwani, S., Endang, J., & Widyantoro, B., 2022, Management of Acute Coronary Syndrome Indonesia : Insight from One ACS Multicenter Registry. Indonesian Journal of Cardiology, 43(2), 45-55. https://doi.org/10.30701/ijc.1406
  34. Kang, L., Zhao, Q., Jiang, K., Yu, X., Chao, H., Yin, L., and Wang, Y., 2023, Uncovering potential diagnostic biomarkers of acute myocardial infarction based on machine learning and analyzing its relationship with immune cells. BMC Cardiovasc Disord 23, 2 (2023). https://doi.org/10.1186/s12872-022-02999-7
  35. Khan, S. Q. Kelly,D., Quinn, P., Favies, J.E., and Ng, L.L., 2007, Myotrophin is a more powerful predictor of major adverse cardiac events following acute coronary syndrome than N-terminal pro-B-type natriuretic peptide, Clinical Science, 112(4), pp. 251–256. doi: 10.1042/CS20060191
  36. Khera R, Haimovich J, Hurley NC, McNamara R, Spertus JA, Desai N, Rumsfeld JS, Masoudi FA, Huang C, Normand SL, Mortazavi BJ, Krumholz HM., 2021, Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction. JAMA Cardiol. 2021 Jun 1;6(6):633-641. doi: 10.1001/jamacardio.2021.0122.PMID:33688915;PMCID:PMC7948114
  37. Kim, M., Kang, D., Kim, M.S., Choe, J.C., Lee, S. Ahn, J.H., Oh, J., Choi, J.H., Lee, H.C., Cha, K.S., Jang,K., Bong, W.I., Song, G., and Lee, H., 2024, Acute myocardial infarction prognosis prediction with reliable and interpretable artificial intelligence system, Journal of the American Medical Informatics Association, Volume 31, Issue 7, July 2024, Pages 1540–1550, https://doi.org/10.1093/jamia/ocae114
  38. Kotsiantis, S. B., 2017, Supervised Machine Learning: A Review of Classification Techniques. Informatica 31, 249-268
  39. Krittanawong, C., Khawaja, M., Tamis‐Holland, J. E., Girotra, S., and Rao, S.V., 2023, Acute Myocardial Infarction: Etiologies and Mimickers in Young Patients, Journal of the American Heart Association, Vol 12, issue 18
  40. Kumar S, Shih CM, Tsai LW, Dubey R, Gupta D, Chakraborty T, Sharma N, Singh AV, Swarup V, Singh HN., 2022, Transcriptomic Profiling Unravels Novel Deregulated Gene Signatures Associated with Acute Myocardial Infarction: A Bioinformatics Approach. Genes (Basel). 2022 Dec 9;13(12):2321. doi: 10.3390/genes13122321.PMID:36553589;PMCID:PMC9777571
  41. Kurniawan, P.R., Setiawan, A.A., Limantoro, C., and Ariosta, A., 2021, The Difference in Troponin I and CK-MB Values in Acute Myocardial Infarction Patient with ST Elevation and Without ST Elevation, Jurnal Kedokteran Diponegoro (Diponegoro Medical Journal), vol. 10, no. 2, pp. 138-144, Mar. 2021. https://doi.org/10.14710/dmj.v10i2.29601
  42. Lei, L. and Bin, Z., 2019, Risk Factor Differences in Acute Myocardial Infarction between Young and Older People: A Systematic Review and Meta-Analysis, 32(2), https://doi.org/10.5935/2359-4802.20190004
  43. Li H, Sun X, Li Z, Zhao R, Li M and Hu T., 2023, Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients. Front. Cardiovasc. Med. 9:1059543. doi: 10.3389/fcvm.2022.1059543
  44. Li W, Yin Y, Quan X, Zhang H., 2019, Gene Expression Value Prediction Based on XGBoost Algorithm. Front Genet. 2019 Nov 12;10:1077. doi: 10.3389/fgene.2019.01077.PMID:31781160;PMCID:PMC6861218
  45. Li, X., Shang, C., Xu, C., Wang, Y., Xu, J., and Zhou, Q., 2023, Development and comparison of machine learning-based models for predicting heart failure after acute myocardial infarction. BMC Med Inform Decis Mak 23, 165 (2023). https://doi.org/10.1186/s12911-023-02240-1
  46. Li Y, He XN, Li C, Gong L, and Liu M., 2019, Identification of Candidate Genes and MicroRNAs for Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis. Biomed Res Int. 2019 Feb 11;2019:5742608. doi: 10.1155/2019/5742608.PMID:30886860;PMCID:PMC6388335
  47. Li, Y.W., Yang, X.D., Cai, Y.C., and Kong, X.Y., 2023, Artificial intelligence-based prediction of acute myocardial infarction mortality risk, 2nd International Conference on Health Big Data and Intelligent Healthcare (ICHIH), Zhuhai, China, 2023, pp. 164-169, doi: 10.1109/ICHIH60370.2023.10396320
  48. Lin, Z., Liu, Y., Gao, Y., Chen, X., Wang, C., Shou, S., and Chai, Y., 2022, S100A9 and SOCS3 as diagnostic biomarkers of acute myocardial infarction and their association with immune infiltration, Genes & Genetic Systems, Volume 97, Issue 2, Pages 67-79
  49. Liu R, Wang M, Zheng T, Zhang R, Li N, Chen Z, Yan H, Shi Q., 2022, An artificial intelligence-based risk prediction model of myocardial infarction. BMC Bioinformatics, 7;23(1):217. doi: 10.1186/s12859-022-04761-4.PMID:35672659;PMCID:PMC9175344
  50. Lu Y, Li SX, Liu Y, Rodriguez F, Watson KE, Dreyer RP, Khera R, Murugiah K, D'Onofrio G, Spatz ES, Nasir K, Masoudi FA, Krumholz HM., 2022, Sex-Specific Risk Factors Associated With First Acute Myocardial Infarction in Young Adults. JAMA Netw Open. 2022 May 2;5(5):e229953. doi: 10.1001/jamanetworkopen.2022.9953.PMID:35503221;PMCID:PMC9066284
  51. Lv, J., Ni, L., Liu, K., Gao, X., Yang, J., Zhang, X., Ye, Y., Dong, Q., Fu, R., Sun, H., Yan, X., Zhao, Y., Wang, Y., Yang, Y., & Xu, H., 2021, Clinical Characteristics, Prognosis, and Gender Disparities in Young Patients With Acute Myocardial Infarction. Frontiers in Cardiovascular Medicine, 8, 720378. https://doi.org/10.3389/fcvm.2021.720378
  52. Mechanic. O.J., Gavin, M., Grossman, S.A., 2023, Acute Myocardial Infarction. 2023 Sep 3. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan–. PMID: 29083808
  53. Miao, M., Cao, S., Tian, Y. , Liu, D., Chen, L., Chai, Q., Wei, M., Sun, S., Wang, L., Xin, S., Liu, G., and Zheng, M., 2023, Potential diagnostic biomarkers: 6 cuproptosis- and ferroptosis-related genes linking immune infiltration in acute myocardial infarction. Genes Immun 24, 159–170 (2023). https://doi.org/10.1038/s41435-023-00209-8
  54. Moore, A., and Bell, M., 2022, XGBoost, A Novel Explainable AI Technique, in the Prediction of Myocardial Infarction: A UK Biobank Cohort Study. Clin Med Insights Cardiol. 8;16:11795468221133611. doi: 10.1177/11795468221133611.PMID:36386405;PMCID:PMC9647306
  55. Mienye, I. D., and Sun, Y., 2022, A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects. IEEA Access, 29-49
  56. Nadya, F., Ferdiansyah, M., Nastiti, V., and Aditya, C., 2024, Implementation of Feature Selection Strategies to Enhance Classification Using XGBoost and Decision Tree. Scientific Journal of Informatics, 11(1), 18-194. doi: https://doi.org/10.15294/sji.v11i1.48145
  57. National Heart, Lung, and Blood Institute, 2022, National Heart, Lung, and Blood Institute. Diambil kembali dari How The Heart Works: https://www.nhlbi.nih.gov/health/heart
  58. Nurlette, F., 2019, Implementasi Metode Gradient Boosting Machine (GBM) untuk Klasifikasi pada Data Bioinformatika, Skripsi, Prodi Statistika, UII, Yogyakarta
  59. Osman, A. H., and Aljahdali, H. M., 2017, An Effective of Ensemble Boosting Learning Method for Breast Cancer Virtual Screening using Neural Network Model. 10.1109/ACCESS.2020.2976149
  60. Pohle, C.L., Ferreira, J.S., Coelho, R. A., Quintal, J., Bernardes, P., Esteves, A.F., Sa, C., and Seixo, F., 2024, Young patients with acute myocardial infarction: early screening is warranted, European Journal of Preventive Cardiology, Volume 31, Issue Supplement_1, zwae175.091, https://doi.org/10.1093/eurjpc/zwae175.091
  61. Permatasari, P.D., Fadil, M., and Syafri, M., 2020, Description of ST Segments Elevation of Myocardial Infarction on Patients Undergoing Primary Percutaneous Coronary Intervention in dr. M. Djamil Hospital Padang, J-Kesmas, 7(1). DOI: https://doi.org/10.35308/j-kesmas.v7i1.1817
  62. Qiu L, and Liu X. 2019, Identification of key genes involved in myocardial infarction. Eur J Med Res. 2019 Jul 3;24(1):22. doi: 10.1186/s40001-019-0381-x.PMID:31269974;PMCID:PMC6607516
  63. Rahman, I. F., 2020, Implementasi Metode SVM, MLP Dan XGBoost Pada Data Ekspresi Gen. (Studi Kasus: Klasifikasi Data Ekspresi Gen Skeletal Muscle Pada Manusia Normal, IGT dan Diabetes Melitus Tipe-2 GSE18732), Skripsi, Prodi Statistika, UII, Yogyakarta
  64. Rizk T. and Blankstein R., 2021, Not All Heart Attacks are Created Equal: Thinking Differently About Acute Myocardial Infarction in the Young, Methodist DeBakey Cardiovasc Journal, 17(4):60-67. doi: 10.14797/mdcvj.345
  65. Roudini, B., Khajehpiri, B., Moghaddam, H.A., and Forouzanfar, M., 2024, Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data, Intelligent Medicine, https://doi.org/10.1016/j.imed.2024.01.001
  66. Shahu, A., Namburar, S., Banna, S., Harris, A., Schenck, C., Trejo-Paredes, C., Thomas, A., Ali, T., Carnicelli, A.P., Barnett, C. F., Solomon, M.A., and Miller, P.E., 2024, Outcomes for Mechanically Ventilated Patients With Acute Myocardial Infarction Admitted to Medical vs Cardiac Intensive Care Units, JACC: Advances, Volume 3, Issue 9, Part 1, 101199, https://doi.org/10.1016/j.jacadv.2024.101199
  67. Shalaby,G., Khaled, S., Jaha, N., Almatrafi, S.A., Alzahrani, S., Melaih, A.B., Mandili, R. A., Al Ahmadi, M. A., and Aboul-Enein, F., 2020, Acute Myocardial Infarction in Young Adults: Prevalence, Clinical Background and In-Hospital Outcomes with Particular Reference to Socio-Economic Influences a Middle Eastern Tertiary Center Experience, Journal of Cardiology Research Reviews & Reports. Vol 1(4):1-8, SRC/JCRRR-129. DOI: https//doi.org/10.47363/JCRR/2020(1)129
  68. Sinha, S., Krishna, V., Thakur, R., Kumar, A., Mishra, V., Jha, M., Singh, K., Sachan, M., Sinha, R., Asif, M., Afdaali, N., and Varma, C., 2017, Acute myocardial infarction in very young adults: A clinical presentation, risk factors, hospital outcome index, and their angiographic characteristics in North India - AMIYA Study. ARYA Atherosclerosis Journal, 13(2), 79-87
  69. Situmorang, P.R., Manurung, F., and Tarigan, R.V.B., 2022, Analysis Of Troponin T Examination Results In Acute Myocard Infark Patients In Santa Elisabeth Hospital Medan, 2022 . Jurnal EduHealth, 13(02), 491–497. Retrieved from https://ejournal.seaninstitute.or.id/index.php/healt/article/view/414
  70. Siwi, A. S., Yudono, D. T., Sebayang, S. M., and Tunis, A., 2023, Efikasi Teknik Relaksasi Benson Pada Skor Nyeri Pasien Acute Myocardial Infarction (AMI). Citra Delima Scientific Journal of Citra Internasional Institute, 7(1), 26–29. https://doi.org/10.33862/citradelima.v7i1.343
  71. Sood A, Singh A, and Gadkari C., 2023, Myocardial Infarction in Young Individuals: A Review Article. Cureus. 2023 Apr 4;15(4):e37102. doi: 10.7759/cureus.37102.PMID:37168155;PMCID:PMC10166330
  72. Soofi, A., and Awan, A., 2017, Classification techniques in machine learning: applications and issues. Journal of Basic and Applied Sciences, pp. 13, 459–465
  73. Sun, Y., Zhong, N., Zhu, X., Fan, Q.,Li, K.,Chen, Y., Wan, X., He, Q., and Xu,Y., 2023, Identification of important genes associated with acute myocardial infarction using multiple cell death patterns, Cellular Signalling, Volume 112, https://doi.org/10.1016/j.cellsig.2023.110921
  74. Suresh, R., Li, X., Chiriac, A., Goel, K., Terzic, A., Perez-Terzic, C., and Nelson, T. J., 2014, Transcriptome from circulating cells suggests dysregulated pathways associated with long-term recurrent events following first-time myocardial infarction. NCBI, 13-21
  75. Suri P, Arora A, Kinra K, and Arora V., 2023, Risk Factors and Angiographic Profile in Young Individuals with Acute ST-Elevation Myocardial Infarction (STEMI). Indian Journal of Clinical Cardiology. 2023;4(4):242-247. doi: 10.1177/26324636231199029
  76. Tarabanis,C., Kalampokis,E., Khalil, M. , Alviar, C.L., Chinitz, L. A., and Jankelson, L., 2023, Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction, Cardiovascular Digital Health Journal;4:126–132
  77. Tarunosudirjo, R., Purwanti, E., and Oktaviano, Y. H., 2020 Early Detection Of Acute Myocardial Infarction Using The Dempster Shafer Method. Indonesian Applied Physics Letters, 1(1), 13–22. https://doi.org/10.20473/iapl.v1i1.21332
  78. Tessy, D. B., Pramudyo, M., and Cool, C. J., 2021, Characteristics of In-Hospital Mortality among Patients with Acute Coronary Syndrome: A Single-Center Study in West Java, Indonesia, Althea Medical Journal, 8 (2), https://doi.org/10.15850/amj.v8n2.2281
  79. Yanase, T., Sakakura, K., Taniguchi, Y., Yamamoto, K., Tsukui, T., Seguchi, M., Wada, H., Momomura, S., and Fujita, H., 2021, Comparison of Clinical Characteristics of Acute Myocardial Infarction Between Young (< 55 Years) and Older (55 to < 70 Years) Patients, International Heart Journal, Volume 62, Issue 1, Pages 33-41, https://doi.org/10.1536/ihj.20-444
  80. Usuda, D., Tanaka, R., Suzuki, M., Takano, H., Hotchi, Y., Shimozawa, S., Tokunaga, S., Osugi, I., Katou, R., Ito, S., Mishima, K., Kondo, A., Mizuno, K., Takami, H., Komatsu, T., Oba, J., Nomura, T., Sugita, M., 2022, ST-Elevation Acute Myocardial Infarction in a Young Man. Journal of Medical Cases, North America, 13
  81. Wahyuningsih, I., Deni, W. M. I., Rahayu, H.T., and Pratiwi, I.D., 2023, Risk Factors for Acute Myocardial Infarction in Young Adults:Literature Review, in 2nd International Conference on Medica lHealth Science,KnEMedicine,pages106–111. DOI 10.18502/kme.v3i2.13042
  82. Wang Y, Chen J, Song W, Wang Y, Chen Y, Nie Y, and Hui, R., 2015, The Human Myotrophin Variant Attenuates MicroRNA-Let-7 Binding Ability but Not Risk of Left Ventricular Hypertrophy in Human Essential Hypertension. PLoS ONE 10(8): e0135526. https://doi.org/10.1371/journal.pone.0135526
  83. Wang, Y., Zhang, X., Duan, M., Zhang, C., Wang, K., Feng, L., Song, L., Wu, S., and Chen, X., 2021, Identification of Potential Biomarkers Associated with Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis, Oxidative Medicine and Cellular Longevity, 5553811, 11 pages, https://doi.org/10.1155/2021/5553811
  84. Wu, Y., Jiang, T., Hua, J., Xiong, Z., Chen, H., Li, L., Peng, J., and Xiong, W., 2022, Integrated Bioinformatics-Based Analysis of Hub Genes and the Mechanism of Immune Infiltration Associated With Acute Myocardial Infarction, Frontiers in Cardiovascular Medicine, Sec. Cardiovascular Genetics and Systems Medicine, Volume 9 - 2022 | https://doi.org/10.3389/fcvm.2022.831605
  85. Xue J, Chen L, Cheng H, Song X, Shi Y, Li L, Xu R, Qin Q, Ma J, Ge J., 2022, The Identification and Validation of Hub Genes Associated with Acute Myocardial Infarction Using Weighted Gene Co-Expression Network Analysis. J Cardiovasc Dev Dis. 2022 Jan 17;9(1):30. doi: 10.3390/jcdd9010030.PMID:35050240;PMCID:PMC8778825
  86. Yang, Q., Ding, J., Luo, Z., and Hu, P., 2023, A bioinformatics approach to the identification of hub genes of Huo Xin Pill (HXP) for the treatment of acute myocardial infarction. Tropical Journal of Pharmaceutical Research. 21. 2651-2658. 10.4314/tjpr.v21i12.21
  87. You, H., and Dong, M., 2023, Identification of Immuno-Inflammation-Related Biomarkers for Acute Myocardial Infarction Based on Bioinformatics. J Inflamm Res. 2023 Aug 7;16:3283-3302. doi: 10.2147/JIR.S421196.PMID:37576155;PMCID:PMC10417757
  88. Zasada, W., Bobrowska, B., Plens, K., Dziewierz, A., Siudak, Z., Surdacki, A., Dudek, D., and Bartus, S., 2021, Acute myocardial infarction in young patients, Kardiologia Polska, 79(10): 1093-1098, DOI: 10.33963/KP.a2021.0099
  89. Zelli, V., Manno, A., Compagnoni, C., Manno, A., Compagnoni, C., Ibraheem, R. O., Zazzeroni, F., Alesse, E., Rossi, F., Arbib, C., and Tessitore, A., 2023, Classification of tumor types using XGBoost machine learning model: a vector space transformation of genomic alterations. J Transl Med 21, 836. https://doi.org/10.1186/s12967-023-04720-4
  90. Zhang, S., Liu, W., Liu, X., Qi, J., and Deng, C., 2017, Biomarkers identification for acute myocardial infarction detection via weighted gene co-expression network analysis. Medicine (Baltimore). 2017 Nov;96(47):e8375. doi: 10.1097/MD.0000000000008375.PMID:29381915;PMCID:PMC5708914
  91. Zhao, E., Xie, H., and Zhang, Y., 2020, Predicting Diagnostic Gene Biomarkers Associated With Immune Infiltration in Patients With Acute Myocardial Infarction, Frontiers in Cardiovascular Medicine, Vol. 7, DOI=10.3389/fcvm.2020.586871

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