# Tabular Data Generation to Improve Classification of Liver Disease Diagnosis
## 7 Experiments and Evaluation
### Evaluation Performance Measures  
The evaluation of the various classifiers on the original dataset before augmentation (No-AUG) after doubling the dataset (DD-AUG) and tripling the dataset (TD-AUG) was conducted using various measures. The Receiver Operating Characteristic (ROC) parameters, including True Positive (TP), False Negative (FN), False Positive (FP), and True Negative (TN), were utilized to evaluate and compare the performance. TP denotes the number of accurate positive diagnoses where the individual is classified as healthy and is indeed healthy; TN denotes the individual who is classified as infected and is indeed infected; and FP denotes the individual who is classified as infected but is healthy. FN denotes the individual who is classified as healthy but is infected. The ROC parameters demonstrate the consistency of the used classifier.  
In addition to the ROC parameters, this study also employs the measures of Sensitivity, Precision, \(F1\), and Specificity to evaluate the model. These performance metrics are derived from the confusion matrices and are calculated using the following equations  
\[Accuracy=\frac{|TN|+|TP|}{|TN|+|TP|+|FN|+|FP|} \tag{1}\]  
\[Specificity=\frac{|TN|}{|TN|+|FP|} \tag{2}\]  
\[Sensitivity=\frac{|TP|}{|TP|+|FN|} \tag{3}\]  
\[F1-Measure=\frac{2*|TP|}{2*|TP|+FN+|FP|} \tag{4}\]  
It is not easy to get all aspects of performance in a contingency table, because only half of its information is used to calculate PPV, N.P.V., Sensitivity and Specificity. The Matthews correlation coefficient (MMC) and the precision factor can benefit from four numbers (all existing numbers), providing a more comprehensive, more balanced and representative representation than if they are linear or vertical. In all of the measures that have been put forward here, we can generalize that a higher value is better, but the MCC method doesn't follow this theory. If we take the values from 0 to 1 (0, \(-\)1, 1), 1 represents perfect correlation, 0 represents random distribution, and \(-\)1 represents perfect negative correlation. In risky cases, class imbalance affects the MCC and the level of accuracy. MCC has been developing slowly compared to others. Of 75% of cases predicted, MCC has achieved only 0.5. Based on random results, 50% of the cases that were predicted correctly (positive and negative) gave a value of 0, unlike other measures that provided 0.5, such as accuracy, N.P.V., Sensitivity, PPV and Specificity. It is reliable to examine performance in data that has been evenly distributed.  
In this form, biases can be considered as deviations from the relationships. We must evaluate all measures to imagine an integrated form of how to perform a prediction. To obtain a clear picture of the performance of the prediction, we can use a receiver operating characteristics (R.O.C.) study. We can analyze this from finding the appropriate classifier. It also illustrates the tradeoffs between the Specificity on one hand and the Sensitivity on the other. There are special programs to draw R.O.C. curves, and we can rely on them when our predictor type is dependent on probabilities and provides a good degree of classification. However, we must be aware that this degree is not a real value for P, but it represents its reliable value in ranking for predictions.  
Sensitivity or recall [38] is one of the reliable measures which are used to verify the ability of a model or system to retrieve the situation in a non-defect case. Depending on the formula's calculations, the score for Sensitivity or recall is calculated between [0, 1] in an interval. However, for Specificity, it is one of the reliable measures to verify the ability of a model or system that cannot retrieve the condition in a non-defect case. Depending on the calculations of this formula, the score of Specificity is calculated between [0, 1] in an interval [39].