| Autor: |
A, Xin, Kangshuo, Li, Lijing L, Yan, Chanchal, Chandramouli, Rundong, Hu, Xurui, Jin, Ping, Li, Mulei, Chen, Geng, Qian, Yundai, Chen |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
International Journal of Cardiology. 375:131-141 |
| ISSN: |
0167-5273 |
| Popis: |
Cardiac magnetic resonance imaging (CMR) is the gold standard for measuring infarct size (IS). However, this method is expensive and requires a specially trained technologist to administer. We therefore sought to quantify the IS using machine learning (ML) based analysis on clinical features, which is a convenient and cost-effective alternative to CMR.We included 315 STEMI patients with CMR examined one week after morbidity in final analysis. After feature selection by XGBoost on fifty-six clinical features, we used five ML algorithms (random forest (RF), light gradient boosting decision machine, deep forest, deep neural network, and stacking) to predict IS with 26 (selected by XGBoost with information gain greater than average level of 56 features) and the top 10 features, during which 5-fold cross-validation were used to train and optimize models. We then evaluated the value of actual and ML-IS for the prediction of adverse remodeling. Our finding indicates that MLs outperform the linear regression in predicting IS. Specifically, the RF with five predictors identified by the exhaustive method performed better than linear regression (LR) with 10 indicators (RML-based methods can predict IS with widely available clinical features, which provide a proof-of-concept tool to quantitatively assess acute phase IS. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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