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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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