Research Article
Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques
Table 2
CHF detection performance based on frequency domain features by applying machine learning techniques.
| Classifier | TPR (%) | TNR (%) | PPV (%) | NPV (%) | TA (%) | AUC | LB | UP |
| Decision tree (DT) | Fine | 83 | 75 | 73 | 85 | 80.2 | 0.84 | 0.17 | 0.75 | Coarse | 93 | 64 | 85 | 81 | 81.9 | 0.81 | 0.07 | 0.64 |
| Support vector machine (SVM) | Linear | 82 | 77 | 72 | 86 | 80.2 | 0.86 | 0.18 | 0.77 | Quadratic | 82 | 82 | 73 | 88 | 81.9 | 0.88 | 0.18 | 0.82 | Cubic | 88 | 68 | 77 | 82 | 80.2 | 0.83 | 0.13 | 0.58 | Med. Gaussian | 90 | 77 | 83 | 87 | 85.3 | 0.90 | 0.16 | 0.77 |
| K-nearest neighbor (KNN) | Fine | 85 | 75 | 75 | 85 | 81.0 | 0.86 | 0.15 | 0.75 | Medium | 89 | 66 | 78 | 81 | 80.2 | 0.88 | 0.11 | 0.66 | Cosine | 64 | 73 | 55 | 79 | 67.2 | 0.75 | 0.36 | 0.73 |
| Ensemble classifiers | Bagged tree | 85 | 77 | 76 | 86 | 81.9 | 0.88 | 0.15 | 0.77 | Subsp. disc. | 86 | 70 | 76 | 83 | 80.2 | 0.85 | 0.14 | 0.70 | RUSBoosted tree | 79 | 75 | 69 | 84 | 77.6 | 0.81 | 0.21 | 0.75 |
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