Machine Learning Methods for Prediction of Hospital Mortality in Patients with Coronary Heart Disease after Coronary Artery Bypass Grafting

Гельцер, Б. И.; Shahgeldyan, K. J.; Rublev, V. Yu.; Kotelnikov, V. N.; Krieger, Axel; Shirobokov, Vladimir G.

doi:10.18087/cardio.2020.10.n1170

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Средние показатели госпитальной смертности после КШ составляют 1,5-3%, но варьируют в зависимости прогнозирования краткосрочной смертности после кардиохирургических операций в сравнении с традиционными шкалами [6][7][8]. Среди отечественных публикаций также имеются единичные исследования, демонстрирующие результаты применениях методов МО для оценки риска при реваскуляризации миокарда [9]. Однако крупных исследований, адаптированных к отечественным условиям оказания кардиохирургической помощи, не проводилось.…”

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Potential of machine learning methods in operational risk stratification in patients with coronary artery disease scheduled for coronary bypass surgery

et al. 2023

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Aim. To develop and evaluate the effectiveness of models for predicting mortality after coronary bypass surgery, obtained using machine learning analysis of preoperative data.Material and methods. As part of a cohort study, a retrospective prediction of in-hospital mortality after coronary artery bypass grafting (CABG) was performed in 2182 patients with stable coronary artery disease. Patients were divided into 2 following samples: learning (80%, n=1745) and training (20%, n=437). The initial ratio of surviving (n=2153) and deceased (n=29) patients in the total sample indicated a pronounced class imbalance, and therefore the resampling method was used in the training sample. Five machine learning (ML) algorithms were used to build predictive risk models: Logistic regression, Random Forrest, CatBoost, LightGBM, XGBoost. For each of these algorithms, cross-validation and hyperparameter search were performed on the training sample. As a result, five predictive models with the best parameters were obtained. The resulting predictive models were applied to the learning sample, after which their performance was compared in order to determine the most effective model.Results. Predictive models implemented on ensemble classifiers (CatBoost, LightGBM, XGBoost) showed better results compared to models based on logistic regression and random forest. The best quality metrics were obtained for CatBoost and LightGBM based models (Precision — 0,667, Recall — 0,333, F1-score — 0,444, ROC AUC — 0,666 for both models). There were following common high-ranking parameters for deciding on the outcome for both models: creatinine and blood glucose levels, left ventricular ejection fraction, age, critical stenosis (>70%) of carotid arteries and main lower limb arteries.Conclusion. Ensemble machine learning methods demonstrate higher predictive power compared to traditional methods such as logistic regression. The prognostic models obtained in the study for preoperative prediction of in-hospital mortality in patients referred for CABG can serve as a basis for developing systems to support medical decision-making in patients with coronary artery disease.

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Potential of machine learning methods in operational risk stratification in patients with coronary artery disease scheduled for coronary bypass surgery

et al. 2023

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“…The plaque arrangement comprises the affidavit of cholesterol, cell byproducts, greasy mixtures, fibrin, and calcium. Factors that add to atherosclerosis incorporate smoking, hypertension, high levels of blood cholesterol, and diabetes in Healthcare (Geltser et al 2020). Cardiovascular infection likewise causes rest apnea and stress Supervised algorithm.…”

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Comparison of Accuracy Rate in Prediction of Cardiovascular Disease using Random Forest with Logistic Regression

Vishnuvardhan¹,

Rama²

2023

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Aim: Comparison of accuracy rate in prediction of cardiovascular disease using Novel Random Forest with Logistic Regression. Materials and Methods: The Novel Random forest (N=20) and Novel Logistic Regression Algorithm (N=20) these two algorithms are calculated by using 2 Groups and taken 20 samples for both algorithm and accuracy in this work.The sample size is determined using the G power Calculator and it’s found to be 10. Results: The Random Forest exhibited 89.06% accuracy whilst a Logistic Regression has shown 92.18%. accuracy. Statistical significance difference between Random forest algorithm and Novel Logistic Regression Algorithm was found to be p=0.001 (2 tailed) (p<0.5). Conclusion: Prediction of cardiovascular disease using Logistic Regression is significantly better than the Random Forest.

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“…вания[5,6]. К перспективным инструментам для отбора таких предикторов относят алгоритмы на основе объяснимого искусственного интеллекта (ОИИ).…”

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Phenotyping of risk factors and prediction of inhospital mortality in patients with coronary artery disease after coronary artery bypass grafting based on explainable artificial intelligence methods

Geltser,

Shakhgeldyan,

Rublev

et al. 2023

Russ J Cardiol

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Aim. To develop predictive models of inhospital mortality (IHM) in patients with coronary artery disease after coronary artery bypass grafting (CABG), taking into account the results of phenotyping of preoperative risk factors.Material and methods. This retrospective study was conducted based on the data of 999 electronic health records of patients (805 men, 194 women) aged 35 to 81 years with a median (Me) of 63 years who underwent on-pump elective isolated CABG. Two groups of patients were distinguished, the first of which was represented by 63 (6,3%) patients who died in the hospital during the first 30 days after CABG, the second — 936 (93,7%) with a favorable outcome. Preoperative clinical and functional status was assessed using 102 factors. Chi-squares, Fisher, Mann-Whitney methods were used for data processing and analysis. Threshold values of predictors were determined by methods, including maximizing the ratio of true positive IHM cases to false positive ones. Multivariate logistic regression (MLR) was used to develop predictive models. Model accuracy was assessed using 3 following metrics: area under the ROC curve (AUC), sensitivity (Sens), and specificity (Spec).Results. An analysis of preoperative status of patients made it possible to identify 28 risk factors for IHM, combined into 7 phenotypes. The latter formed the feature space of IHM prognostic model, in which each feature demonstrates the patient’s compliance with a certain risk factor phenotype. The author’s MLR model had high quality metrics (AUC-0,91; Sen-0,9 and Spec-0,85).Conclusion. The developed data processing and analysis algorithm ensured high quality of preoperative risk factors identification and IHM prediction after CABG. Prospects for further research on this issue are related to the improvement of explainable artificial intelligence technologies, which allow developing information systems for managing clinical practice risks.

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Machine Learning Methods for Prediction of Hospital Mortality in Patients with Coronary Heart Disease after Coronary Artery Bypass Grafting

Cited by 5 publications

References 11 publications

Potential of machine learning methods in operational risk stratification in patients with coronary artery disease scheduled for coronary bypass surgery

Potential of machine learning methods in operational risk stratification in patients with coronary artery disease scheduled for coronary bypass surgery

Comparison of Accuracy Rate in Prediction of Cardiovascular Disease using Random Forest with Logistic Regression

Phenotyping of risk factors and prediction of inhospital mortality in patients with coronary artery disease after coronary artery bypass grafting based on explainable artificial intelligence methods

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