Research Article
A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms
Table 6
10-fold CV classification performance evaluation of different classifiers on Cleveland heart disease dataset on full features.
| Predictive model | Classifiers performance evaluation metrics | Accuracy (%) | Specificity (%) | Sensitivity (%) | MCC | AUC (%) | Processing time (s) |
| Logistic regression (C = 10) | 84 | 85 | 83 | 89 | 84 | 19.213 | K-nearest neighbor (K-NN, K = 9) | 76 | 74 | 73 | 76 | 73 | 29.400 | Artificial neural network (13, 16, 2) | 74 | 73 | 74 | 50 | 69 | 21.600 | SVM (kernel = RBF, C = 100, = 0.0001) | 86 | 88 | 78 | 85 | 86 | 15.234 | SVM (kernel = linear) | 75 | 78 | 75 | 78 | 74 | 18.239 | Naive Bayes | 83 | 87 | 78 | 80 | 84 | 34.101 | Decision tree | 74 | 76 | 68 | 75 | 76 | 21.911 | Random forest (100) | 83 | 70 | 94 | 82 | 83 | 15.121 |
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