Research Article
Machine Learning Approach to Extract Diagnostic and Prognostic Thresholds: Application in Prognosis of Cardiovascular Mortality
Table 3
Performance of classifiers.
| Classifiers | Sensitivity | Specificity | Accuracy |
| I f systolic ARV ≥ 9.6 then 1 Else 0 | 55.7% | 60.4% | 59.9% | If systolic WBP ≥ 134.6 then 1 Else 0 | 52.5% | 58.8% | 58.08% | If systolic ARV ≥ 9.6 and systolic WBP ≥ 137 then 1 Else 0 | 36.1% | 80.0% | 75.1% | If systolic ARV ≥ 9.6 and systolic WBP ≥ 138.6 and cholesterol ≥ 5.5 then 1 Else 0 | 8.2% | 93.3% | 83.8% | If systolic ARV ≥ 10.4 and systolic WBP ≥ 139.8 and BMI ≥ 27.3 then 1 Else 0 | 9.8% | 93.3% | 84.0% | If systolic ARV ≥ 9.6 and systolic WBP ≥ 137 and diastolic WBP ≥ 78.4 then 1 Else 0 | 22.9% | 87.5% | 80.4% | Naïve Bayes | 11.48% | 95.92% | 86.57% |
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