Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

Roudini, Bijan; Khajehpiri, Boshra; Moghaddam, Hamid Abrishami; Forouzanfar, Mohamad

doi:10.1016/j.imed.2024.01.001

Intelligent Medicine

2024

DOI: 10.1016/j.imed.2024.01.001

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Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

Bijan Roudini,

Boshra Khajehpiri,

Hamid Abrishami Moghaddam

et al.

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

Fajriyah,

Isnandar,

Arifuddin

2024

Medstat

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

Fajriyah,

Isnandar,

Arifuddin

2024

Medstat

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Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

Cited by 1 publication

References 34 publications

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

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

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