.png){kind=icon}
返回
Stroke classification based on deep reinforcement learning over stroke screening imbalanced data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2023.109069.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Stroke screening is a crucial measure for reducing stroke occurrence, disability, and mortality. However there are numerous risk factors and the limited number of high-risk stroke groups in all screening populations, the screening data is redundant and imbalanced. We propose an improved feature selection algorithm to identify stroke key risk factors and an oversampling MRF-SMOTE algorithm to balance data. Then, a deep reinforcement learning classification model based on the Dueling DQN (Deep Q-network) algorithm is constructed for stroke classification, with the optimized loss function. Benchmark models include KNN, SVM, random forest, and Dueling DQN. Stroke screening data is pre-processed by selecting key risk factors and oversampled by MRFSMOTE. Experiments show that the optimized Dueling DQN model can classify the preprocessed stroke screening data in terms of accuracy, AUC, precision, and F1-measure, which are 0.8982, 0.96, 0.899, and 0.8981 and improved by 4.37 %, 6.92 %, 3.78 % and 4.04 %, respectively, compared with the existing Dueling DQN.
Keyword:
Stroke classification
Stroke key risk factors selection
Deep reinforcement learning model
Imbalanced data
期刊
C
IF:
4.9
论文数:
6.7K
被引数:
1.3W
机构
引用论文
A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset
.png){kind=icon}
PREAI
分享收藏
Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏Detection and vascular territorial classification of stroke on diffusion-weighted MRI by deep learning基于深度学习的弥散加权MRI卒中检测及血管区域分类
PREAI
分享收藏
查看更多内容