# Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing
## 3 Summary of Results  
A summary of the results for the five selected classes (51 (Hemiplegia), 112 (Kidney stones), 184 (Herpangina), 188 (Bacterial middle ear infection) and 135 (Pneumonia))--see Figures 7 and 8--allows the identification of the classification methods that showed the highest performance and those that achieved the best results most often. Class 51 (Hemiplegia) In this class, the best results for all indicators (Sensitivity, Specificity, Balanced Accuracy) were achieved by the classifiers LogisticRegression, RandomForest, AdaBoost, ExtraTreesClassifier and SGDClassifier, with values close to 0.97 for Balanced Accuracy. The standard deviation coefficients for these classifiers were also at relatively low levels, indicating stability in performance. Class 112 (Kidney stones) In the 112 class, ExtraTreesClassifier and LogisticRegression achieved the highest scores, with a Balanced Accuracy of 0.96 and 0.94, respectively. The RandomForest, GausianNB, AdaBoost and Perceptron classifiers also achieved a high Balanced Accuracy of 0.94. The standard deviations for these classifiers indicate a relatively low level of variability in the results. Class 184 (Herpangina) In this class, the performance of RandomForest and ExtraTreesClassifier was the best, achieving a Balanced Accuracy of 0.99, with stable results (Sensitivity and Specificity equal to 1.0 and 0.97). LogisticRegression and kNN also performed well, achieving a Balanced Accuracy of 0.98 and 0.97, respectively. 188 (Bacterial middle ear infection) class In the 188 class, the classifiers ExtraTreesClassifier, RandomForest, GradientBoosting and BaggingClassifier were the most effective and achieved a Balanced Accuracy of 0.95-0.97. kNN and LogisticRegression also scored highly but slightly lower than the leaders, with a Balanced Accuracy of 0.94. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Classifier** & **Sensitivity** & **Specificity** & **Balanced Accuracy** & **Sensitivity SD** & **Specificity SD** & **Balanced Accuracy SD** \\ \hline GausianNB & 0.91 & 0.97 & 0.94 & 0.02 & 0.05 & 0.02 \\ LogisticRegression & 1.0 & 0.87 & 0.93 & 0.0 & 0.09 & 0.05 \\ Perceptron & 0.99 & 0.87 & 0.93 & 0.01 & 0.12 & 0.06 \\ SGDClassifier & 0.99 & 0.86 & 0.93 & 0.01 & 0.15 & 0.07 \\ ExtraTreesClassifier & 1.0 & 0.84 & 0.92 & 0.0 & 0.09 & 0.05 \\ BaggingClassifier & 0.99 & 0.83 & 0.91 & 0.0 & 0.09 & 0.04 \\ RandomForest & 1.0 & 0.8 & 0.9 & 0.0 & 0.1 & 0.05 \\ GradientBoosting & 1.0 & 0.81 & 0.9 & 0.0 & 0.11 & 0.06 \\ kNN & 1.0 & 0.79 & 0.89 & 0.0 & 0.1 & 0.05 \\ DecisionTree & 0.99 & 0.79 & 0.89 & 0.01 & 0.12 & 0.06 \\ AdaBoost & 0.99 & 0.78 & 0.89 & 0.01 & 0.09 & 0.05 \\ SVM & 1.0 & 0.67 & 0.83 & 0.0 & 0.13 & 0.06 \\ SVC & 1.0 & 0.67 & 0.83 & 0.0 & 0.13 & 0.06 \\ \hline \end{tabular}
\end{table} 
Table 6: Classification result for Central Class 135 (pneumonia) vs. all other classes. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Classifier** & **Sensitivity** & **Specificity** & **Balanced Accuracy** & **Sensitivity SD** & **Specificity SD** & **Balanced Accuracy SD** \\ \hline ExtraTreesClassifier & 1.0 & 0.93 & 0.97 & 0.0 & 0.07 & 0.04 \\ RandomForest & 1.0 & 0.9 & 0.95 & 0.0 & 0.09 & 0.04 \\ GradientBoosting & 1.0 & 0.9 & 0.95 & 0.0 & 0.08 & 0.04 \\ BaggingClassifier & 1.0 & 0.91 & 0.95 & 0.0 & 0.08 & 0.04 \\ kNN & 0.99 & 0.89 & 0.94 & 0.01 & 0.07 & 0.03 \\ LogisticRegression & 1.0 & 0.89 & 0.94 & 0.0 & 0.1 & 0.05 \\ DecisionTree & 0.99 & 0.89 & 0.94 & 0.0 & 0.09 & 0.05 \\ Perceptron & 0.99 & 0.9 & 0.94 & 0.01 & 0.1 & 0.05 \\ AdaBoost & 0.99 & 0.86 & 0.93 & 0.0 & 0.08 & 0.04 \\ SGDClassifier & 1.0 & 0.87 & 0.93 & 0.0 & 0.12 & 0.06 \\ GausianNB & 0.99 & 0.85 & 0.92 & 0.01 & 0.11 & 0.06 \\ SVM & 1.0 & 0.79 & 0.89 & 0.0 & 0.12 & 0.06 \\ SVC & 1.0 & 0.79 & 0.89 & 0.0 & 0.12 & 0.06 \\ \hline \end{tabular}
\end{table} 
Table 5: Classification result for Central Class 188 (bacterial middle ear infection) vs. all other classes.