Prediction Model of Heart Disease Diagnosis Based on Borderline-SMOTE and OOA-SVM
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.
Borderline-SMOTEOsprey Optimization Algorithmsupport vector machinediagnostic prediction of heart disease
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