A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary syndrome.

Syed Waseem Abbas Sherazi,Jang-Whan Bae,Jong Yun Lee,Saurav Chatterjee

doi:10.1371/journal.pone.0249338

Syed Waseem Abbas Sherazi, Jang-Whan Bae + Show 2 more

Open Access

https://doi.org/10.1371/journal.pone.0249338

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Journal: PloS one	Publication Date: Jun 11, 2021
Citations: 45	License type: CC BY 4.0

Affiliation: Chungbuk National University

Abstract

ObjectiveSome researchers have studied about early prediction and diagnosis of major adverse cardiovascular events (MACE), but their accuracies were not high. Therefore, this paper proposes a soft voting ensemble classifier (SVE) using machine learning (ML) algorithms.MethodsWe used the Korea Acute Myocardial Infarction Registry dataset and selected 11,189 subjects among 13,104 with the 2-year follow-up. It was subdivided into two groups (ST-segment elevation myocardial infarction (STEMI), non ST-segment elevation myocardial infarction NSTEMI), and then subdivided into training (70%) and test dataset (30%). Third, we selected the ranges of hyper-parameters to find the best prediction model from random forest (RF), extra tree (ET), gradient boosting machine (GBM), and SVE. We generated each ML-based model with the best hyper-parameters, evaluated by 5-fold stratified cross-validation, and then verified by test dataset. Lastly, we compared the performance in the area under the ROC curve (AUC), accuracy, precision, recall, and F-score.ResultsThe accuracies for RF, ET, GBM, and SVE were (88.85%, 88.94%, 87.84%, 90.93%) for complete dataset, (84.81%, 85.00%, 83.70%, 89.07%) STEMI, (88.81%, 88.05%, 91.23%, 91.38%) NSTEMI. The AUC values in RF were (98.96%, 98.15%, 98.81%), ET (99.54%, 99.02%, 99.00%), GBM (98.92%, 99.33%, 99.41%), and SVE (99.61%, 99.49%, 99.42%) for complete dataset, STEMI, and NSTEMI, respectively. Consequently, the accuracy and AUC in SVE outperformed other ML models.ConclusionsThe performance of our SVE was significantly higher than other machine learning models (RF, ET, GBM) and its major prognostic factors were different. This paper will lead to the development of early risk prediction and diagnosis tool of MACE in ACS patients.

Highlights

From the past few decades, mortality rate of patients with acute coronary syndrome (ACS) has increased [1] and It has become the leading cause of mortality all over the world [2]
The performance of our soft voting ensemble classifier (SVE) was significantly higher than other machine learning models (RF, extra tree (ET), gradient boosting machine (GBM)) and its major prognostic factors were different
We compared the primary prognostic factors by using the machine learning-based models named as random forest, extra tree, gradient boosting machine, and soft voting ensemble classifier

Summary

Introduction

From the past few decades, mortality rate of patients with acute coronary syndrome (ACS) has increased [1] and It has become the leading cause of mortality all over the world [2]. According to World Health Organization, acute coronary syndrome is the topmost cause of death worldwide. In Korea, it has become the leading cause of mortality. Acute coronary syndrome is a death causing disease where ST-elevation myocardial Infarction is more fatal than the non-STelevation myocardial infarction [3]. Diagnosis of acute coronary syndrome and prediction of STEMI and NSTEMI traces is very crucial for the patients affected by heart diseases. It is very difficult to accurately predict the solemnity of acute coronary syndrome from the medical dataset as it is dependent on multiple risk factors

Methods

Results

Conclusion