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Data analytics approach for short- and long-term mortality prediction following acute non-ST-elevation myocardial infarction (NSTEMI) and Unstable Angina (UA) in Asians

Author

Listed:
  • Sazzli Kasim
  • Putri Nur Fatin Amir Rudin
  • Sorayya Malek
  • Firdaus Aziz
  • Wan Azman Wan Ahmad
  • Khairul Shafiq Ibrahim
  • Muhammad Hanis Muhmad Hamidi
  • Raja Ezman Raja Shariff
  • Alan Yean Yip Fong
  • Cheen Song

Abstract

Background: Traditional risk assessment tools often lack accuracy when predicting the short- and long-term mortality following a non-ST-segment elevation myocardial infarction (NSTEMI) or Unstable Angina (UA) in specific population. Objective: To employ machine learning (ML) and stacked ensemble learning (EL) methods in predicting short- and long-term mortality in Asian patients diagnosed with NSTEMI/UA and to identify the associated features, subsequently evaluating these findings against established risk scores. Methods: We analyzed data from the National Cardiovascular Disease Database for Malaysia (2006–2019), representing a diverse NSTEMI/UA Asian cohort. Algorithm development utilized in-hospital records of 9,518 patients, 30-day data from 7,133 patients, and 1-year data from 7,031 patients. This study utilized 39 features, including demographic, cardiovascular risk, medication, and clinical features. In the development of the stacked EL model, four base learner algorithms were employed: eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), with the Generalized Linear Model (GLM) serving as the meta learner. Significant features were chosen and ranked using ML feature importance with backward elimination. The predictive performance of the algorithms was assessed using the area under the curve (AUC) as a metric. Validation of the algorithms was conducted against the TIMI for NSTEMI/UA using a separate validation dataset, and the net reclassification index (NRI) was subsequently determined. Results: Using both complete and reduced features, the algorithm performance achieved an AUC ranging from 0.73 to 0.89. The top-performing ML algorithm consistently surpassed the TIMI risk score for in-hospital, 30-day, and 1-year predictions (with AUC values of 0.88, 0.88, and 0.81, respectively, all p

Suggested Citation

  • Sazzli Kasim & Putri Nur Fatin Amir Rudin & Sorayya Malek & Firdaus Aziz & Wan Azman Wan Ahmad & Khairul Shafiq Ibrahim & Muhammad Hanis Muhmad Hamidi & Raja Ezman Raja Shariff & Alan Yean Yip Fong & , 2024. "Data analytics approach for short- and long-term mortality prediction following acute non-ST-elevation myocardial infarction (NSTEMI) and Unstable Angina (UA) in Asians," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-28, February.
  • Handle: RePEc:plo:pone00:0298036
    DOI: 10.1371/journal.pone.0298036
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    References listed on IDEAS

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    1. Joon-myoung Kwon & Ki-Hyun Jeon & Hyue Mee Kim & Min Jeong Kim & Sungmin Lim & Kyung-Hee Kim & Pil Sang Song & Jinsik Park & Rak Kyeong Choi & Byung-Hee Oh, 2019. "Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-15, October.
    2. Sazzli Kasim & Sorayya Malek & Cheen Song & Wan Azman Wan Ahmad & Alan Fong & Khairul Shafiq Ibrahim & Muhammad Shahreeza Safiruz & Firdaus Aziz & Jia Hui Hiew & Nurulain Ibrahim, 2022. "In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-28, December.
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