A biomarker discovery of acute myocardial infarction using feature selection and machine learning

Abstract

Acute myocardial infarction (AMI) or heart attack is a significant global health threat and one of the leading causes of death. The evolution of machine learning has greatly revamped the risk stratification and death prediction of AMI. In this study, an integrated feature selection and machine learning approach was used to identify potential biomarkers for early detection and treatment of AMI. First, feature selection was conducted and evaluated before all classification tasks with machine learning. Full classification models (using all 62 features) and reduced classification models (using various feature selection methods ranging from 5 to 30 features) were built and evaluated using six machine learning classification algorithms. The results showed that the reduced models performed generally better (mean AUPRC via random forest (RF) algorithm for recursive feature elimination (RFE) method ranges from 0.8048 to 0.8260, while for random forest importance (RFI) method, it ranges from 0.8301 to 0.8505) than the full models (mean AUPRC via RF: 0.8044). The most notable finding of this study was the identification of a five-feature model that included cardiac troponin I, HDL cholesterol, HbA1c, anion gap, and albumin, which had achieved comparable results (mean AUPRC via RF: 0.8462) as to the models that containing more features. These five features were proven by the previous studies as significant risk factors for AMI or cardiovascular disease and could be used as potential biomarkers to predict the prognosis of AMI patients. From the medical point of view, fewer features for diagnosis or prognosis could reduce the cost and time of a patient as lesser clinical and pathological tests are needed.

Keywords: Acute myocardial infarction; Biomarker; Classification; Feature selection; Heart attack; Machine learning.

Fig. 1

The proposed framework for biomarker discovery in AMI using machine learning and feature selection. Abbreviations: AMI, acute myocardial infarction; ANN, artificial neural network; AUPRC, area under the precision-recall curve; DT, decision tree; kNN, k-nearest neighbours; LR, logistic regression; PCC, Pearson’s coefficient correlation; RF, random forest; RFE, recursive feature elimination; RFI, random forest importance; SVM, support vector machine (refer to Appendix A for the full list of abbreviations)

Fig. 2

Performance evaluation from one of the runs in RFI-13 features a reduced model with RF as a classifier on the testing set a confusion matrix; b PRC and AUPRC of the 5-CV (mean PRC in blue line)