Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost  

作　　者：Congjun Rao Mengxi Li Tingting Huang Feiyu Li 

机构地区：[1]School of Science,Wuhan University of Technology,Wuhan,430070,China [2]Wuhan University of Technology Hospital,Wuhan University of Technology,Wuhan,430070,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第4期699-724,共26页工程与科学中的计算机建模（英文）

基　　金：supported by the National Natural Science Foundation of China (No.72071150).

摘　　要：Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

关 键 词：STROKE risk assessment decision-making CatBoost feature selection borderline SMOTE Logistic-AB 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]