Table 4 The comparison of each model for CVD prediction results
From: A risk factor attention-based model for cardiovascular disease prediction
Model | Accuracy % | Precision % | Recall % | F-score % | \(|\Delta |\) |
|---|---|---|---|---|---|
\(SVM_{(raw)}\) | 90.91 | 90.91 | 90.91 | 90.91 | 4.98 |
\(SVM_{(no\ labels)}\) | 89.39 | 89.03 | 89.39 | 89.21 | 6.64 |
\(ConvNets_{(raw)}\) | 92.83 | 92.64 | 92.83 | 92.73 | 3.13 |
\(ConvNets_{(risks\ with\ labels)}\) | 93.94 | 89.43 | 93.21 | 91.28 | 3.93 |
\(LSTM_{(raw)}\) | 92.24 | 93.46 | 92.73 | 93.09 | 3.01 |
\(LSTM_{(risks\ with\ labels)}\) | 82.58 | 81.35 | 83.01 | 82.17 | 13.61 |
\(RFAB_{(raw,\ no\ att)}\) | 93.91 | 93.83 | 93.91 | 93.86 | 2.01 |
\(RFAB_{(risks\ with\ labels,\ no\ att)}\) | 89.23 | 88.96 | 89.23 | 89.07 | 6.77 |
\(RFAB_{(no\ labels)}\) | 95.43 | 95.39 | 95.43 | 95.41 | 0.48 |
RFAB | 95.87 | 95.98 | 95.87 | 95.86 | – |