# Glucose Data Classification for Diabetic Patient Monitoring
## 7 Result and Discussion  
In this section, we show and discuss the test results of our proposed system; the plan for this research is to evaluate the performance of methods for classifying diabetes data. The data is evaluated using the J48, naive Bayes, RandomTree, and ZeroR algorithms. We used a dataset that included 30 patients for a total of 30 days: 23 men and 7 women aged 40 to 60 years. The dataset used for the purpose of analysis had a total of 2700 instances, in which there were 2070 instance of men detected and 630 of women detected. The format of this dataset contains five columns designated by date, day, glucose level, and request. This research work deals mainly with the accuracy of classification algorithms with respect to execution time and error rate with WEKA software [19]. To classify diabetes data appropriately from the calculation dataset, error rates and accuracy are calculated using classifiers. The results of various measurements are given in Table 2.  
This section of the paper focuses on predicting the possible number of diabetic patients from the dataset using data classification techniques and determining which model gives the highest percentage of correct predictions for the diagnoses. These techniques are required for prediction of diabetes in patients. This research has the major goal of predicting the number of diabetics and comparing the best available methods of prediction. Therefore, we can determine what attribute is the best predictor for the correct prediction of the diagnoses, and we can help experts to predict possible diabetic attacks from the patient datasets. From the test results, as shown in Table 1, it can be stated that the measurement data were successfully sent. Among 20 times of testing, the range of delay between transmitting and receiving SMS was from 22 to 91 s, and the average was 32.17 s. Figure 9 shows a graph of the measurement of the level of glucose in the blood. \begin{table}
\begin{tabular}{c c c} \hline
**Day** & **Morning** & **Evening** \\ \hline
1 & 1.34 & 0.78 \\
2 & 2.43 & 0.87 \\
3 & 1.11 & 1.02 \\
4 & 2.45 & 0.55 \\
5 & 0.88 & 1.43 \\
6 & 1.00 & 0.78 \\
7 & 0.99 & 1.34 \\
8 & 1.32  
The test of the dataset was analyzed by the use of four algorithms classification, which were the naive Bayes, J48, ZeroR, and RandomTree (using training set). In addition, a comparison of the accuracy of all classifiers was done and finally, it was investigated that the RandomTree technique performed better with an accuracy of 99.6732%. The accuracy level of all the algorithms is given in Table 2.  
As seen in Table 2, the comparison between the algorithms like ZeroR, RandomTree, J48, and naive Bayes for the correctly classified instances was higher than the incorrectly classified instances in terms of accuracy. Figure 10 shows a bar graph that represents correctly and incorrectly ranked instances of the algorithms used. Figure 11 shows the time graph of various classification algorithms. The longest time was taken by ZeroR consuming a time of 0.05 s and the shortest time was taken by RandomTree consuming 0.01 s only. Figure 10: The graph of correctly and incorrectly classified instances of algorithms. Figure 9: Measurement of the level of glucose in the blood. Figure 12 represents Kappa statistic, MAE (mean absolute error), and RMSE (root mean squared error) values for the chosen classifiers. The naive Bayes algorithm showed better results for MAE and RMSE, and the J48 algorithm showed a better value for the Kappa statistic. Figure 13 shows the decision tree built by the classifier, which includes the root nodes, child nodes, and leaf nodes with their possible/predicted values. Figure 11: The graph for training time results for the top four classifiers. Figure 12: KAPPA statistics and error rate values. In the above section, all the rules were interpreted. These rules were visualized in the tree form. Tree visualization is the simplest method to understand the conditions and their results.