Title: Improving prediction of one-year mortality of acute myocardial infarction using machine learning techniques

Authors: Mirza Touseef; Najla Raza; Adeel Zafar; Muhammad Zubair; Saad Zafar

Addresses: Information Services Department, Riphah International University, I-14 Campus, Islamabad, Pakistan ' Faculty of Computing, Riphah International University, I-14 Campus, Islamabad, Pakistan ' Faculty of Computing, Riphah International University, I-14 Campus, Islamabad, Pakistan ' Faculty of Computing, Riphah International University, I-14 Campus, Islamabad, Pakistan ' Faculty of Computing, Riphah International University, City Campus – I, G-7/4, 7th Avenue, Islamabad, Pakistan

Abstract: The purpose of our study was to improve the prediction of one-year mortality for patients with acute myocardial infarction (AMI). We implemented and compared four classical machine learning algorithms and one deep neural network algorithm. For evaluation metrics, we used accuracy, F1-measure, precision, recall, and area under receiver operating curve (AUC). Random forest achieved the best performance based on an AUC of 0.98 with an accuracy of 92%. Results show that our model can predict one-year mortality of AMI with an improved AUC and accuracy using a minimum number of features as compared to previous related studies.

Keywords: machine learning; deep neural networks; acute myocardial infarction; AMI; mortality prediction; cardiovascular diseases; CVDs.

DOI: 10.1504/IJMEI.2023.130729

International Journal of Medical Engineering and Informatics, 2023 Vol.15 No.3, pp.221 - 231

Received: 03 Dec 2020
Accepted: 12 Apr 2021
Published online: 04 May 2023 *

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