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

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.

中国股票市场操纵识别研究——基于机器学习分类算法

本文整理了2006—2021年证监会行政处罚涉及的股票市场操纵案例,并通过Wilcoxon秩和检验来筛选构造解释变量,之后综合运用各种过采样算法和机器学习模型对其进行实证分析。研究发现:第一,经过过采样算法扩充样本的模型预测精度明显大于样本不平衡的模型;第二,综合比较各种过采样算法,Borderline-SMOTE过采样算法的预测精度大于SMOTE和ADASYN过采样算法;第三,综合比较各类机器学习分类模型,SVM模型的预测精度明显大于其他机器学习分类模型。本文结论对股票市场操纵的及早识别预测,促进资本市场良性发展具有一定的理论意义和实践价值。