首页|Second Hospital of Dalian Medical University Reports Findings in Heart Disease (Machine learning-based models for prediction of the risk of stroke in coronary artery disease patients receiving coronary revascularization)
Second Hospital of Dalian Medical University Reports Findings in Heart Disease (Machine learning-based models for prediction of the risk of stroke in coronary artery disease patients receiving coronary revascularization)
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New research on Heart Disorders and Diseases - Heart Disease is the subject of a report. According to news reporting out of Liaoning, People’s Republic of China, by NewsRx editors, research stated, “To construct several prediction models for the risk of stroke in coronary artery disease (CAD) patients receiving coronary revascularization based on machine learning methods. In total, 5757 CAD patients receiving coronary revascularization admitted to ICU in Medical Information Mart for Intensive Care Ⅳ (MIMIC-IV) were included in this cohort study.” Financial support for this research came from Dalian Medical Science research project. Our news journalists obtained a quote from the research from the Second Hospital of Dalian Medical University, “All the data were randomly split into the training set (n = 4029) and testing set (n = 1728) at 7:3. Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO) regression model were applied for feature screening. Variables with Pearson correlation coefficient ＜9 were included, and the regression coefficients were set to 0. Features more closely related to the outcome were selected from the 10-fold cross-validation, and features with non-0 Coefficent were retained and included in the final model. The predictive values of the models were evaluated by sensitivity, specificity, area under the curve (AUC), accuracy, and 95% confidence interval (CI). The Catboost model presented the best predictive performance with the AUC of 0.831 (95%CI: 0.811-0.851) in the training set, and 0.760 (95%CI: 0.722- 0.798) in the testing set. The AUC of the logistic regression model was 0.789 (95%CI: 0.764-0.814) in the training set and 0.731 (95%CI: 0.686-0.776) in the testing set. The results of Delong test revealed that the predictive value of the Catboost model was significantly higher than the logistic regression model (P ＜0.05). Charlson Comorbidity Index (CCI) was the most important variable associated with the risk of stroke in CAD patients receiving coronary revascularization.”
LiaoningPeople’s Republic of ChinaAsiaAngiologyArterial Occlusive DiseasesArteriosclerosisCardiologyCardiovascular Diseases and ConditionsCerebrovascular Diseases and ConditionsCoronary ArteryCoronary Artery DiseaseCyborgsEmerging TechnologiesHealth and MedicineHeart DiseaseHeart Disorders and DiseasesMachine LearningMyocardial IschemiaRisk and PreventionStroke
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