عنوان انگلیسی مقاله:
Using machine learning to predict one-year cardiovascular events in patients with severe dilated cardiomyopathy
ترجمه فارسی عنوان مقاله:
استفاده از یادگیری ماشینی برای پیش بینی وقایع یک ساله قلبی و عروقی در بیماران دارای کاردیومیوپاتی اتساع شدید
Sciencedirect - Elsevier - European Journal of Radiology, 117 (2019) 178-183: doi:10:1016/j:ejrad:2019:06:004
Rui Chena,b,1, Aijia Luc,1, Jingjing Wanga,b, Xiaohai Mac, Lei Zhaoc, Wanjia Wua, Zhicheng Dud, Hongwen Feie, Qiongwen Line, Zhuliang Yuf,⁎⁎, Hui Liua,b
Purpose: Dilated cardiomyopathy (DCM) is a common form of cardiomyopathy and it is associated with poor
outcomes. A poor prognosis of DCM patients with low ejection fraction has been noted in the short-term followup.
Machine learning (ML) could aid clinicians in risk stratification and patient management after considering
the correlation between numerous features and the outcomes. The present study aimed to predict the 1-year
cardiovascular events in patients with severe DCM using ML, and aid clinicians in risk stratification and patient
Materials and Methods: The dataset used to establish the ML model was obtained from 98 patients with severe
DCM (LVEF < 35%) from two centres. Totally 32 features from clinical data were input to the ML algorithm,
and the significant features highly relevant to the cardiovascular events were selected by Information gain (IG).
A naive Bayes classifier was built, and its predictive performance was evaluated using the area under the curve
(AUC) of the receiver operating characteristics by 10-fold cross-validation.
Results: During the 1-year follow-up, a total of 22 patients met the criterion of the study end-point. The top
features with IG > 0.01 were selected for ML model, including left atrial size (IG=0.240), QRS duration
(IG=0.200), and systolic blood pressure (IG=0.151). ML performed well in predicting cardiovascular events
in patients with severe DCM (AUC, 0.887 [95% confidence interval, 0.813–0.961]).
Conclusions: ML effectively predicted risk in patients with severe DCM in 1-year follow-up, and this may direct
risk stratification and patient management in the future.
Keywords: Severe dilated cardiomyopathy | Prognostic value | Machine learning