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Abstract #2643

A Machine Learning Approach for Predicting cardiovascular event in HCM patient on Cardiac MRI

kankan hao1,2, yanjie zhu1,2, dong liang1,2, shihua zhao3, xin liu1,2, and hairong zheng1,2
1Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, shenzhen, China, 2University of Chinese Academy of Sciences, Beijing, China, 3Department of Magnetic Resonance Imaging, Fuwai Hospital and National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, beijing, China

Cardiac magnetic resonance(CMR) is a highly reliable measurement to assess and predict cardiovascular events. The traditional regression model need a linear assumption but it can not be guaranteed. We use a ML method to predict cardiovascular events in HCM patients. According to our result, the C statistic for the ML model (0.804 [95% CI, 0.757-0.850]) was higher than Cox regression model (0.764, [95% CI, 0.671-0.857]). With the random sample, the ROC for the ML model(0.96 in the training set, 0.83 in the test set) was higher than the regression model(0.80 in the training set, 0.79 in the test set).

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