基于Borderline-SMOTE和OOA-SVM的心脏病诊断预测模型
Prediction Model of Heart Disease Diagnosis Based on Borderline-SMOTE and OOA-SVM
祖璇 1张广海2
作者信息
- 1. 芜湖学院经济系,安徽芜湖 241008
- 2. 芜湖学院大数据与人工智能系,安徽芜湖 241008
- 折叠
摘要
为实现心脏病精准预测,构建了一种预测准确率较高的心脏病诊断预测模型.首先对原始数据集进行pearson相关性分析和归一化处理;然后采用过采样技术Borderline-SMOTE算法,平衡训练数据集的少数类;之后利用鱼鹰优化算法(Osprey Optimization Algorithm,OOA)优化支持向量机(support vector machine,SVM),获得最优参数组合(C,g);最后在测试数据集上进行分类预测.与SSA-SVM、SMA-SVM和SVM相比,本文方法OOA-SVM的预测准确率最高,达到了95.08％,且模型稳定性最好.
Abstract
In order to realize the accurate prediction of heart disease,a prediction model of heart disease diagnosis with high prediction accuracy was established.Firstly,pearson corre-lation analysis and normalization were performed on the original data set;and then the over-sampling Borderline-SMOTE algorithm was used for balance a few classes of the training data set;then the Osprey Optimization Algorithm(OOA)was used to optimize the support vector machine(SVM)to obtain the optimal parameter combination(C,g);finally,classification prediction is made on the test data set.Compared with SSA-SVM,SMA-SVM and SVM,the prediction accuracy of OOA-SVM is the highest,reaching 95.08％,and the model stability is the best.
关键词
Borderline-SMOTE/鱼鹰优化算法/支持向量机/心脏病诊断预测Key words
Borderline-SMOTE/Osprey Optimization Algorithm/support vector machine/diagnostic prediction of heart disease引用本文复制引用
出版年
2025
NETL
NSTL
万方数据