## 4 Comparison between Medical Data Classification Methods
### Results
In Table 5, the results show that in the heart disease dataset, the J48 algorithm takes a long time for processing with 85.5 s. In addition, the NB algorithm takes the lowest processing time with 0.078 s, but it has a minimum accuracy value of 77.5%  
Tables 8 and 9 show the comparison between different classification algorithms on the diabetes dataset before and after preprocessing. The tables present the accuracy, relative error, precision, sensitivity, and time of each algorithm. In Tables 8 and 9, the results show that the accuracy after preprocessing is better than without preprocessing for all of the tested algorithms. Moreover, for time processing, the time processing of the algorithms without preprocessing is longer than the time processing of the preprocessed data. Figures 4 and 5 show the representation of accuracy, precision, sensitivity, and standard deviation (SD) for the heart disease dataset. Figures 6 and 7 show the comparison between the accuracy and time results of the data before and after preprocessing. In Figures 6 and 7, the results show that the accuracy after preprocessing is better than without preprocessing for all of the tested algorithms on the heart disease dataset. Moreover, for time processing, the time processing of the algorithms without a preprocessing is longer than the time processing of the preprocessed data. Figure 5: The SD values of heart disease data. Figure 6: Accuracy comparison of different algorithms on heart disease before and after preprocessing. Figure 7: Time comparison of different algorithms on heart disease before and after preprocessing. In Figures 10 and 11, the results show that the accuracy after preprocessing is better than without preprocessing for all of the tested algorithms on the general health dataset. Moreover, for time processing, the time processing of the algorithms without preprocessing is longer than the time processing of the preprocessed data. Figures 12 and 13 show the representation of accuracy, precision, sensitivity, and SD for the diabetes dataset. Figures 14 and 15 show the comparison between the accuracy and time results of the data before and after preprocessing. The standard deviation (SD) is a descriptive statistic that measures the amount of variation or dispersion in a set of numbers. A low SD implies that the values are close to the mean of a set (also known as the expected value), whereas a high SD shows that the values are spread over a larger range. In Figures 14 and 15, the results show that the accuracy after preprocessing is better than without preprocessing for all of the tested algorithms on the diabetes dataset. Moreover, for time processing, the time processing of the algorithms without a preprocessing is longer than the time processing of preprocessed data. The performance results are calculated using the following equations:  
\[\textbf{Precision=}\frac{\textbf{TP}}{\textbf{TP+FP}} \tag{3}\]  
\[\textbf{Accuracy=}\frac{\textbf{TP+TN}}{\textbf{TP+TN+FP+FN}} \tag{4}\]  
\[\textbf{Sensitivity, Recall}\;(\textbf{TP rate})\textbf{=}\frac{\textbf{TP}}{ \textbf{TP+FN}} \tag{5}\]  
\[\textbf{Relative Error=}\frac{\textbf{Absolute error}*\textbf{100\%}}{\textbf{True}^{ \textbf{\"value}}} \tag{6}\]  
where TP is the true positive value, TN is the true negative value, FP is the false positive value, and FN is the false negative value. Figure 14: Accuracy comparison of different algorithms on diabetes disease before and after preprocessing. Figure 15: Time comparison of different algorithms on general health before and after preprocessing. With regards to the diabetes dataset, the data have increased, and thus the processing time of the experimented algorithms increased. The highest accuracy is for the RF algorithm with a 97.58% value, but the lowest accuracy is 73.828% for the J48 algorithm. The J48 accuracy decreases when the data increase.