# A Novel Approach for Fraud Detection in Blockchain-Based Healthcare Networks Using Machine Learning
## 4 Performance Evaluation
### Scenario 1: Dataset 1  
Table 2 lists the evaluation results of the proposed system using the algorithms according to the human vital signs dataset. A human vital signs dataset would typically include information about a person's key physiological indicators that help assess their basic body functions [24]. These vital signs are essential for determining the overall health status of an individual. The most common vital signs include:  
* Heart rate (HR): The number of times a person's heart beats per minute;
* Blood pressure (BP): The force exerted by circulating blood on the walls of blood vessels, usually measured as systolic and diastolic pressures;
* Respiratory rate (RR): The number of breaths a person takes per minute;
* Body temperature (Temp): The internal temperature of the body, usually measured in degrees Celsius or Fahrenheit;
* Oxygen saturation (SpO2): The percentage of oxygen-bound hemoglobin in the blood, indicating how well oxygen is being transported to various parts of the body. We obtained the best results after the features selection method because it improves the model's performance. Feature selection is a technique in machine learning that selects the most important and relevant features of input data, while discarding less important ones to improve the model's accuracy and efficiency. It also helps remove noise in the data, improving the model's ability to identify patterns and make accurate predictions. This technique is particularly important for dealing with large and complex datasets [36]. Accordingly, we illustrated the achieved accuracy of each algorithm in Figure 7. Table 2 and Figure 8 demonstrate that Random Forest achieved the highest accuracy (99.82), which means it is the best method for solution in this part of the proposed system. are labeled based on their characteristics, such as high-value transfers between addresses that have no real-world identity associated with them or multiple low-value transfers to obscure addresses. This dataset can be used for building predictive models using supervised learning techniques to classify new incoming transactions into either fraudulent or legitimate ones based on past patterns observed in historical datasets [25]. This dataset represents transactions inside the blockchain network, and obtained the results in terms of accuracy, precision, recall, and F1-score, as shown in Table 3. Accuracy is considered an important parameter to evaluate the system; Figure 8 illustrates and compares the accuracy of each algorithm with other algorithms to determine the best method for solution in this part of the proposed system. Figure 8: Accuracy of the algorithms using dataset 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Logistic & 84.05 & 88.48 & 32.26 & 47.2 \\ \hline KNN & 95.68 & 92.6 & 87.51 & 89.97 \\ \hline SVM & 84.55 & 80.55 & 39.88 & 53.34 \\ \hline Gaussian NB & 30.88 & 23.99 & 97.88 & 38.54 \\ \hline Decision Tree & 97.02 & 93.2 & 93.39 & 93.28 \\ \hline Random Forest & 98.2 & 98.64 & 93.16 & 95.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation of the proposed system using dataset 2. Figure 7: Accuracy of the algorithms using dataset 1. Figure 9 compares the execution time of machine learning algorithms. The \(x\)-axis represents the name of the algorithms, while the \(y\)-axis represents the time in seconds. By looking at the figure, we can observe that the Naive Bayes, decision tree, and Random Forest algorithm achieved a short execution time, which means speed and best performance from the other algorithms. As mentioned above, Receiver Operating Characteristic-Area Under Curve (ROC-AUC) measures the ability of a binary classifier to distinguish between positive and negative classes across all possible threshold settings. Figure 10 displays the (ROC-AUC) of machine learning algorithms. The \(x\)-axis represents the name of the algorithms, while the \(y\)-axis represents the range (0-1).