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©The Author(s) 2022.
World J Cardiol. Nov 26, 2022; 14(11): 565-575
Published online Nov 26, 2022. doi: 10.4330/wjc.v14.i11.565
Published online Nov 26, 2022. doi: 10.4330/wjc.v14.i11.565
Table 5 Comparison of the models for the prediction of an abnormal cardiac stress test
| Feature | BR | RF | XGBoost |
| Prediction cutoff value | 0.20 | 0.18 | 0.27 |
| Sensitivity (95%CI) | 45.06 (44.23, 45.88) | 13.92 (13.50, 14.33) | 30.54 (29.30, 31.79) |
| Specificity (95%CI) | 80.46 (80.14, 80.79) | 93.66 (93.53, 93.80) | 88.51 (88.15, 88.86) |
| PPV (95%CI) | 21.34 (21.09, 21.60) | 20.55 (20.05, 21.04) | 24.33 (23.46, 25.20) |
| NPV (95%CI) | 92.55 (92.42, 92.69) | 90.24 (90.14, 90.35) | 91.34 (91.12, 91.56) |
- Citation: Shafiq M, Mazzotti DR, Gibson C. Risk stratification of patients who present with chest pain and have normal troponins using a machine learning model. World J Cardiol 2022; 14(11): 565-575
- URL: https://www.wjgnet.com/1949-8462/full/v14/i11/565.htm
- DOI: https://dx.doi.org/10.4330/wjc.v14.i11.565