Volume 31, Issue 146 (May & June 2023)                   J Adv Med Biomed Res 2023, 31(146): 210-220 | Back to browse issues page

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Shojaeifard M, Ahangar H, Gohari S, Oveisi M, Maleki M, Reshadmanesh T, et al . Assessment of Machine Learning Approaches to Predict in-Hospital Mortality in Patients Underwent Prosthetic Heart Valve Replacement Surgery. J Adv Med Biomed Res 2023; 31 (146) :210-220
URL: http://journal.zums.ac.ir/article-1-6918-en.html
Assessment of Machine Learning Approaches to Predict in-Hospital Mortality in Patients Underwent Prosthetic Heart Valve Replacement Surgery
Maryam Shojaeifard1 , Hassan Ahangar *2 , Sepehr Gohari3 , Mehrdad Oveisi4 , Majid Maleki1 , Tara Reshadmanesh3 , Shahram Arsang-Jang5 , Mahsa Mahjani6 , Mozhgan Pourkeshavarz7 , Ghasem Hajianfar8 , Saeedeh Mazloomzadeh7 , Isaac Shiri8 , Sheida Gohari9
1- Echocardiography Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
2- Dept. of Cardiology, Mousavi Hospital, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran , hassan.ahangar@yahoo.com
3- Student Research Center, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
4- Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King’s College London, London, United Kingdom
5- Dept. of Biostatistics, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
6- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
7- Dept. of Biomedical and Health Informatics, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
8- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
9- Dept. of Systems Science and Industrial Engineering, State University of New York at Binghamton, Binghamton, New York, United States
Abstract:   (3467 Views)

Background and Objective: Machine learning and artificial intelligence are useful tools to analyze data with multiple variables. It has been shown that the prediction models obtained by Machine learning have better performance than the conventional statistical methods. This study was aimed to assess the risk factors and determine the best machine learning prediction model/s for in-hospital mortality among patients who underwent prosthetic valve replacement surgery.
Materials and Methods: In this retrospective cross-sectional study, patient’s pre-operative, intra-operative and post-operative data underwent univariate analysis. Feature importance determination was carried out using algorithms including principal component analysis (PCA), support vector machine (SVM), random forest (RF) model-based, and recursive feature elimination (RFE).  Then, 13 machine learning classifiers were implemented for in-hospital prediction model.
Results: The In-hospital mortality rate was 6.36%. Data from 2455 patients underwent final analysis. The machine learning results revealed that among pre-operative features, Adaptive boost (AB) and RF classifiers (AUC: 0.82±0.033; 0.78±0.028, respectively); among intra-operative features, AB and K-nearest neighbors (KNN) classifiers (AUC: 0.68±0.014); among postoperative features, AB and RF classifiers (AUC: 0.9±0.1; 0.88±0.095, respectively); and among all features, AB and LR classifiers (AUC: 0.93±0.049; 0.93±0.055, respectively) had the best performance in prediction of in-hospital mortality.
Conclusion: The AB classifier was determined as the best model in prediction of in-hospital mortality in all 4 datasets.

Keywords: Prosthetic valve replacement, In-hospital mortality, Risk factor, Machine learning
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✅ The AB classifier was determined as the best model in prediction of in-hospital mortality in all 4 datasets.


Type of Study: Original Research Article | Subject: Clinical Medicine
Received: 2022/12/5 | Accepted: 2023/04/19 | Published: 2023/06/26
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