## 4 Experiments and Results
### Performance Evaluation Parameter  
The proposed model deals with the classification and detection of drivers having diabetes. Classification algorithms of this study classify our feature set individually based on correctly classified instances and incorrectly classified instances by evaluating the true positive (TP) rate, true negative (TN) rate, false negative (FN) rate, and false positive (FP) rate. TP and TN show the numbers of diabetic patients that were accurately classified, and FN and FP demonstrate the quantities of diabetic patients that were erroneously classified. Table 5 shows the training time with the frequency of correctly classified and incorrectly classified instances, including the TP and FP rates of all algorithms. The algorithms compare the different results on the same dataset over multiple machine learning algorithms to evaluate the efficiency of the algorithms. Figure 8 shows the training time of the algorithms, as mentioned above. Naive Bayes and random forest take the shortest time, i.e., 0.11 s, whereas simple logistic takes the longest time, about 7.89 s. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Algorithms**} & \multirow{2}{*}{**Training Time (s)**} & **Correctly** & **Incorrectly** & \multirow{2}{*}{**FP Rate (\%)**} & \multirow{2}{*}{**TP Rate (\%)**} \\  & & **Classified** & **Classified** & & \\  & & **Instances (\%)** & **Instances (\%)** & & \\ \hline Naive Bayes & 0.11 & 77.6699 & 22.3301 & 11.5 & 77.7 \\ SMO & 4.53 & 93.9806 & 6.0194 & 6.1 & 94.0 \\ Simple Logistic & 7.89 & 93.9806 & 6.0194 & 7.1 & 94.0 \\ Classification via & 4.53 & 89.8058 & 10.1942 & 4.0 & 89.8 \\ Regression & 4.77 & 90.0 & 10.0 & 9.5 & 90.0 \\ Decision Table & 0.97 & 89.0291 & 10.9709 & 4.7 & 89.0 \\ Random Forest & 0.11 & 95.0485 & 4.9515 & 8.5 & 95.0 \\ JRip & 3.06 & 77.6699 & 22.3301 & 6.0 & 92.1 \\ \hline \end{tabular}
\end{table} 
Table 5: Training time and accuracy level of the algorithms. Figure 9 presents a comparison between all algorithms of this study in terms of correctly classified instances and incorrectly classified instances. TP specifies the ability of all algorithms to accurately predict diabetes, whereas FP specifies the knowledge of all algorithms, which accurately predict those not having diabetes, respectively. Figure 10 shows the TP and FP rates of all algorithms. Figure 8: The training time of the algorithms. Figure 9: Rate of correctly classified instances and incorrectly classified instances. The performance measurements of the classification model are estimated depending on the evaluation parameters introduced. Accuracy [13] defines the ratio of correctly predicted diabetes cases out of all instances, and precision defines how many of those predicted as diabetic have diabetes. TP specifies the ability of all algorithms to accurately predict diabetes, whereas FP specifies the ability of all algorithms, which accurately predict those not having diabetes, respectively. \[\text{Accuracy}=\frac{\text{TN}+\text{TP}}{\text{TN}+\text{TP}+\text{FP}+ \text{FN}} \tag{9}\]  
Precision [13] defines how many of those who are predicted as having diabetes are diabetic patients. \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{10}\]  
Recall [13] defines all the diabetic patients and how many are correctly predicted. \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{11}\]  
whereas the F1-score [32], also recognized as F-measure, is the harmonic mean of precision and recall. \[\text{F-measure}/\text{F-score}=\frac{2\times\text{recall}\times\text{ precision}}{\text{recall}+\text{precision}} \tag{12}\]  
Mean squared error (MSE) [32] measures the mean squared difference between predicted diabetes cases and actual cases. \[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(y_{i}\ -\bar{y}_{i})^{2} \tag{13}\]  
Mean absolute error (MAE) [33] measures the number of errors in classification. \[\text{MAE}=\frac{\sum_{i=1}^{n}|y_{i}-x_{i}|}{n} \tag{14}\]  
[PERSON]'s kappa [34] measures the performance of the model.