Liuyi Yao, Zijun Yao, et al.
ICHI 2021
Atrial fibrillation (AF) is a common cardiac arrhythmias, which increases the risk and severity of ischemic stroke. For predicting ischemic stroke in AF patients, a risk prediction model that can achieve both good model discrimination (e.g., A UC) and statistical significance ofpredictors is required in real clinical practices. In this paper, we propose a new bootstrap-based wrapper (Boots-wrapper) method of feature selection, and apply this method on Chinese Atrial Fibrillation Registry data to develop 1-year stroke prediction models in AF. The proposed method can heuristically search a subset of features to maximize the discrimination of the prediction model and minimize the penalty for the non-significant features. To achieve robust feature selection, we perform bootstrap sampling to get a more reliable estimate of the variation and significance statistics. The experimental results show that Boots-wrapper can balance model discrimination and statistical significance offeatures for developing AF stroke prediction models.
Liuyi Yao, Zijun Yao, et al.
ICHI 2021
Bin Liu, Ying Li, et al.
IEEE TKDE
Yuan Zhang, Chang-Sheng Ma, et al.
ICHI 2019
Soumya Ghosh, Yu Cheng, et al.
ICHI 2016