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The effectiveness of machine learning in predicting early postoperative death after coronary bypass surgery

Authors: Golukhova E.Z., Keren M.A., Zavalikhina T.V., Bulaeva N.I., Grechishnikova D.A., Sigaev I.Yu., Yakhyaeva K.B.

Company: Bakoulev National Medical Research Center for Cardiovascular Surgery, Moscow, Russian Federation

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Type:  Original articles


DOI: https://doi.org/10.24022/1997-3187-2023-17-1-77-93

For citation: Golukhova E.Z., Keren M.A., Zavalikhina T.V., Bulaeva N.I., Grechishnikova D.A., Sigaev I.Yu., Yakhyaeva K.B. The effectiveness of machine learning models in predicting early postoperative death after coronary bypass surgery. Creative Cardiology. 2023; 17 (1): 77–93 (in Russ.). DOI: 10.24022/1997-3187-2023-17-1-77-93

Received / Accepted:  09.01.2023 / 13.03.2023

Keywords: machine learning coronary artery bypass grafting death prediction



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Abstract

Objective. To develop and evaluate the effectiveness of models for predicting mortality after coronary bypass surgery versus EuroSCORE II.

Material and methods. The analysis included anonymised medical data of 2,826 patients with stable IHD after coronary artery bypass grafting (CABG). Mortality in the group was 1.45% (n = 41). The target feature for prediction was death from any cause after CABG. To predict death after CABG, logistic regression (LR) and gradient boosting methods were used: LightGBM, XGBoost, CatBoost and ensemble model (EM), which consisted of all four developed models. The results of the models were compared with risk model EuroSCORE II. Precision, recall, F1-score and AUC were used to evaluate the effectiveness.

Results. The LR model had the highest ability to predict the outcomes of a positive class among the general class (recall – 0.88), but had the lowest accuracy value (precision – 0.03). Gradient boosting models (LightGBM, XGBoost, Catboost and EM), had higher accuracy precision and acceptable recall and F1-score values. EM had the highest predictive efficiency when comparing models (recall – 0.50, precision – 0.67, F1-measure – 0.57, AUC – 0.85). The EuroSCORE II did not show sufficient effectiveness in predicting the risk of death after CABG in the analyzed patients (recall – 0.143, precision – 0.125, F1-measure – 0.133, AUC – 0.47). The best discrimination indicators were obtained for the LightGBM (AUC – 0.84), Catboost (AUC – 0.87) and EM (AUC – 0.85) models.

Conclusion. We have developed models for predicting death after coronary bypass surgery using machine-learning methods (LR, LightGBM, XGBoost, CatBoost and EM). The quality metrics of models developed using machine learning methods, especially gradient boosting methods, exceed the predictive capabilities of the EuroSCORE II.

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About Authors

  • Elena Z. Golukhova, Dr. Med. Sci., Professor, Academician of RAS, Director; ORCID
  • Milena A. Keren, Dr. Med. Sci., Senior Researcher; ORCID
  • Tatyana V. Zavalikhina, Cand. Med. Sci., Deputy Chief Physician; ORCID
  • Naida I. Bulaeva, Cand. Biol. Sci., Senior Researcher, Cardiologist, Head of Department; ORCID
  • Darya A. Grechishnikova, Cand. Phys. Tech. Sci.; ORCID
  • Igor Yu. Sigaev, Dr. Med. Sci., Professor, Head of Department; ORCID
  • Kumushdzhan B. Yakhyaeva, Cardiologist; ORCID

Chief Editor

Leo A. Bockeria, MD, PhD, DSc, Professor, Academician of Russian Academy of Sciences, President of Bakoulev National Medical Research Center for Cardiovascular Surgery


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