# Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing
## 3 Summary of Results
Class 135 (Pneumonia) The results of class 135 show that GaussianNB and LogisticRegression scored highest, with a Balanced Accuracy of 0.94 and 0.93, respectively. Classifiers such as Perceptron and SGDClassifier also scored high, with a Balanced Accuracy of 0.93 for both. RandomForest and GradientBoosting scored slightly lower at around 0.90. Overall summary In summary results, the ExtraTreesClassifier, RandomForest, LogisticRegression and GradientBoosting classifiers usually achieved the best results across the different classes. ExtraTreesClassifier and RandomForest were particularly often at the top in terms of Balanced Accuracy in most classes. LogisticRegression also showed stable and high scores, and GradientBoosting regularly ranked in the top with high Balanced Accuracy values and relatively low standard deviations. Figure 8: It shows the standard deviations for sensitivity, specificity and balanced accuracy, demonstrating the stability of the results. The largest deviations are seen for specificity and balanced accuracy, indicating the diversity of results in these indicators for the selected classes. Figure 7: 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). It shows the average values of sensitivity (Sensitivity), specificity (Specificity) and balanced accuracy (Balanced Accuracy) for each classifier. We can see that most classifiers achieve high values, especially for sensitivity, with slightly more variation in specificity. Best methods for the classes under consideration: For most of the classes in the file (e.g., 51 (Hemiplegia), 112 (Kidney stones), 184 (Herpangina), 188 (Bacterial middle ear infection), 135 (Pneumonia), and other classes), the following methods performed best: ExtraTreesClassifier-Often achieved the highest Balanced Accuracy in the range 0.96-1.0 and showed low standard deviations, indicating stability of results. RandomForest--Also achieved high scores, often with Balanced Accuracy in the 0.95-0.99 range, making it an effective and stable classifier. LogisticRegression--Often achieved high scores, with Balanced Accuracy in the range 0.90-0.98, with moderate standard deviations. GradientBoosting--Regularly ranked at the top, achieving a Balanced Accuracy of 0.90-0.98 in most classes, showing stability with low to moderate standard deviations. AdaBoost and BaggingClassifier--Achieved high scores in several classes, with Balanced Accuracy values up to 0.97, but with slightly higher deviations in some cases. The most promising techniques ExtraTreesClassifier and RandomForest can be considered the most effective and promising techniques. They achieved high Balanced Accuracy scores in almost every class, demonstrating their versatility and reliability. In addition, the low standard deviations suggest that the performance of these classifiers is statistically stable and hardly susceptible to random fluctuations in results. Statistical validity based on standard deviation The low standard deviations (0.02-0.05) in the ExtraTreesClassifier and RandomForest results indicate the statistical validity of their results and their robustness to variation in the data. Not only do these techniques achieve high Balanced Accuracy values, but they also show consistency, which is important in applications where stability of results is crucial. In conclusion, ExtraTreesClassifier and RandomForest are the most promising techniques for all classes considered due to their high performance and stability of results.