Computer-Aided Diagnosis of Acute Myocardial Infarction using Time-Dependent Plasma Metabolites

Abstract

Acute myocardial infarction (MI) is complicated, and multiple etiologies can result in this clinical condition. Guidelines recognize two categories of MI: Thrombotic (Type 1) and non-thrombotic (Type 2), that have quite same prevalence but require unlike treatment. Unfortunately, diagnostic criteria to differentiate between Type 1 and Type 2 require invasive procedures. This results in inefficient and sub-optimal care of patients suspected of MI. This paper presents a novel machine-learning system that detects biomarkers of thrombus formation by analyzing the association between plasma metabolites with the formation of thrombosis in cohort of MI patients at multiple time-points. Study data are collected by a newly introduced non-targeted technique that evaluates the quantities of both known and unknown metabolites from blood samples. Our system uses recursive feature elimination (RFE) and multi-layer perceptron (MLP) neural network to detect associated metabolites at each time-point followed by weighted-voting algorithm using ensemble learning. Our experiment achieves an accuracy of 91%, sensitivity of 89%, and specificity of 94% for MI diagnosis.