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Table 4 Comparisons on the sensitivity, specificity and accuracy of classification among different machine learning approaches. Performances greater than 85% are highlighted in bold

From: A parsimonious approach for screening moderate-to-profound hearing loss in a community-dwelling geriatric population based on a decision tree analysis

  Sensitivity Specificity Accuracy
Better-hearing ear Training set (Community A) Decision Tree (DT) 95.35% 86.85% 91.20%
Support Vector Machine (SVM) 100.00% 100.00% 100.00%
Random Forest (RF) 91.32% 94.97% 93.10%
Multilayer Perceptron (MLP) 78.29% 56.98% 67.88%
Test set (Community B) Decision Tree (DT) 93.50% 90.56% 91.92%
Support Vector Machine (SVM) 100.00% 33.57% 64.29%
Random Forest (RF) 94.31% 92.31% 93.23%
Multilayer Perceptron (MLP) 90.65% 51.05% 69.36%
Worse-hearing ear Training set (Community A) Decision Tree (DT) 97.53% 71.54% 89.93%
Support Vector Machine (SVM) 100.00% 100.00% 100.00%
Random Forest (RF) 98.21% 77.51% 92.15%
Multilayer Perceptron (MLP) 99.89% 11.38% 73.99%
Test set (Community B) Decision Tree (DT) 97.58% 80.69% 91.17%
Support Vector Machine (SVM) 100.00% 34.16% 75.00%
Random Forest (RF) 99.39% 84.65% 93.80%
Multilayer Perceptron (MLP) 100.00% 19.31% 69.36%