# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
## 4 Performance Evaluation
### Effect of DT Prediction  
The prediction concept is based on substituting a specific I-cwnd into the improved approaches. The selection of the optimal I is based on the conduct of the performance factors, where maximum cwnd, throat, and que represent the ideal outcome, in addition to a limited packet loss. Each I represents one of the I-alternative congestion control parameters. The branches below each node can define the conditions to determine the optimal and non-optimal nodes, where N refers to non-optimal and O refers to optimal in Figure 5.  
The DT prediction in Figure 6 presents a tree graph after applying the J48 (C4.5) DT algorithm on the enhanced SCTP congestion control alternative. The root node is cwnd, and the arches represent the conditions of value comparison to determine the optimal node. The N leaf represents a non-optimal node, and optimal nodes are represented by \"O\". If cwnd was less than or equal to 133, then the node was considered as N. Besides, the N represents the number of nodes that match the condition. If cwnd was greater than 133 and the queue size was less than or equal to 50,639, then six cases that matching this condition were considered as N nodes. If que was greater than 50,639 and throat was less than or equal to 9,881,687, then 70 cases were considered as N nodes. If throat was greater than 9,881,687 and pkt loss was less than or equal to 222, then the nodes would be considered as O.  
Fourteen cases satisfied this condition, and two cases were misclassified. In the final leaf branch, when the packet loss was less than 22, eight cases were considered non-optimal, and one case was misclassified. Based on this road map of DT and the IF-THEN statement used to find the optimal node, the best optimal nodes among all nodes were 3 and 4. I = 3 had a cwnd of 145, a throat of 9,905,462,  
Figure 5: Determination of optimal alternatives. Figure 6: Congestion control DT. a que of 53,788, and a pkt loss of 24. I = 4 had a cwnd of 200, a throu of 10,120,778, a que of 57,674, and a pkt loss of 27. These nodes satisfied all conditions and achieved the optimal factors that determine the optimal node among all nodes. The roadmap in the figure is helpful for manually detecting the optimal nodes by dropping the parameter readings on the tree. After 80% of the dataset was divided into a training set and the remaining 20% as test set, the model was reevaluated to find the prediction margin in order to prove the accuracy of the DT prediction model. Figure 7 shows the margin score, which is a number that measures the accuracy of the predicted alternative, the prediction margin of each predicted node, and the accuracy measure of the prediction model. Margin scores range between 0 and 1; when the score is near 1 it means that the prediction is 100% accurate and when it nears 0 it indicates an incorrectly predicted alternative. Actual alternative (AA) represents whether an alternative is optimal or non-optimal. Predicted alternative (PA) represents the predicted case of the alternative after applying the DT algorithm. The figure demonstrates the accuracy of the DT prediction model, where the \(y\)-axis shows the margin scope of the prediction margin, and the \(x\)-axis represents the test alternatives by which the alternatives are shown as the actual alternatives and the predicted alternatives above them. These actual cases of alternatives were taken from the test set by which the alternatives were either O or N. The predicted alternatives represent the result of DT prediction by which the model predicted the alternative as either N or O. The prediction margin is a value that falls between 1 and \(-1\). If the prediction value is 1, it means that the predicted value is 100% correct. When the value reaches 1, the prediction accuracy becomes perfect, and when the values reaches \(-1\), it means that the predicted value is incorrect. The DT prediction model predicted the optimal node with 100% accuracy. The non-optimal alternatives were predicted with an average rate of 96%.