Cardiovascular disease (CVD) caused by atherosclerosis is one of the major causes of death world-wide. Currently, diverse machine learning models have been applied to disease prediction and classification. However, most of them tend to focus on the performance of the algorithm and neglect the underlying variables for patients in different carotid atherosclerotic stages. In this paper, we propose a novel hybrid machine learning method named Subset Learning (S-learning) to predict and discover the risk factors of these different stages. The S-learning algorithm can elucidate the variables that have significant influence on the outcome of carotid atherosclerotic. Performance comparisons are based on the dataset collected from both Shanghai Renji and Shanghai Huashan Hospital. The result shows that the proposed method has superior classification performance than other classification algorithms. Our findings point to the utility of predictive machine learning and the discovery of risk factors to refine the treatment plans.
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(8.0+极速模式)