## 4 Experiments and Results
### Experimental Environment  
We implement the proposed methodology in NS-2.33 to evaluate the proposed model. We employed the whole scenario in the NS-2 simulator with an area range of 1400 m \(\times\) 1400 m. The experimental parameters and settings are shown in Table 4. To evaluate the performance of the proposed protocol, we evaluate the custom network topology, which has interaction points, vehicles, highways, RSUs, source vehicles, and destination vehicles. Weka (Waikato environment for knowledge analysis) is used for evaluation and classification. Several worthy classification techniques have been proposed in the past for diabetes management, i.e., decision tree (DT), random forest (RF), naive Bayes (NB), sequential minimal optimization (SMO), logistic regression (LR), etc. It has been found that no classification algorithm performs well with different evaluation parameters and 10-fold cross-validation; hence, the study of numerous classifiers is useful. Classification assigns categories to data collection to predict and analyze more accurately. Classification is performed based on a set of characteristics or features.  
We explored all the existing methods and proposed a new approach for predicting diabetes based on genetic features. We also performed a comparative analysis among existing classification algorithms for the best possible performance. We test our data by using eight different algorithms, including naive Bayesian (NB), sequential minimal optimization (SMO), simple logistics (SL), classification via regression (CvR), decision tree (DT), OneR, random forest (RF), and JRip. We found that all the classifiers, as mentioned above, predict differently and generate different results. All the computed parameters  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Parameters** & **Values** \\ \hline Simulator Used & NS-2.34 \\ \hline Network Area Range & Highway of 1400 m \(\times\) 1400 m \\ \hline Total Simulation Time & 350 ms \\ \hline Nodes Density & 10, 20, 30, 40, 50, 60, 70, 80 \\ \hline Transmission Range Among Vehicles & 260 m \\ \hline Number of Vehicles & 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 \\ \hline Number of RSUs & 10 (1 per 10 vehicles) \\ \hline Network Connectivity & Wi-Fi \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation Setup in VANET.  
are unified and fragmented into train and test sets by applying n-fold cross-validation where n = 10. To validate our result, we place emphases on accuracy, precision, recall, and F-measure. However, we not only compare all algorithms used in this study but also our results with recent research works using all performance metrics of their studies, i.e., sensitivity, specificity, MCC, and AROC.  
We use the cross-validation method to evaluate our model, focusing on this study's eight classification algorithms. The itemized after-effects of every expectation model are introduced one after the other.  
Dataset Description  
The supervised machine learning technique required labeled datasets for training and testing purposes. The numerical results are based on a real dataset that demonstrates the effectiveness of the proposed content pre-caching scheme. We are working on a novel technique to solve the problem of driver's diabetes monitoring. The dataset is obtained and generated using the VANET environment in NS-2. The main characteristics of the dataset are indicated using Table 3 with sample values. There are 115,585 records of the dataset. A total of 40% dataset is used for training, and the remaining 60% is used for testing purposes.