# A Novel Approach for Fraud Detection in Blockchain-Based Healthcare Networks Using Machine Learning
## 4 Performance Evaluation
False Positive (FP): The number of times falsely predicted, where the actual value was negative, but the model predicted a positive value;3. True Negative (TN): The number of times the actual value matches the predicted value. The actual value was negative, and the model predicted a negative value;
4. False Negative (FN): The number of times falsely predicted, where the actual value was positive, but the model predicted as a negative value. **Actual values** 1. Accuracy: It refers to the percentage of correct classifications a trained machine learning model achieves. It is the number of correct predictions divided by the total number of predictions across all classes, as in the following equation: \[\textbf{Accuracy}=\frac{\textbf{TP}+\textbf{TN}}{\textbf{TP}+\textbf{FP}+\textbf {FN}+\textbf{TN}}\] (1)
2. Precision: We used precision to calculate the model's ability to classify positive values correctly. It represents the true positives divided by the total number of predicted positive values as in the following equation: \[\textbf{Precision}=\frac{\textbf{TP}}{\textbf{TP}+\textbf{FP}}\] (2)
3. Recall: It refers to how many actual positive cases the model could predict correctly. The recall is the true positives divided by the total number of actual positive values. \[\textbf{Recall}=\frac{\textbf{TP}}{\textbf{TP}+\textbf{FN}}\] (3)
4. F1-Score: It is the harmonic mean of recall and precision. It is useful when you need to combine them, and it has the maximum value when precision is equal to recall, as in the following equation: \[\textbf{F1}-\textbf{score}=\frac{2\times\textbf{Precision}\times\textbf{Recall }}{\textbf{Precision}+\textbf{Recall}}\] (4)
5. Execution time: execution time is the amount of time it takes for an algorithm to complete its operation and produce its output, including data pre-processing, data splitting, and model evaluation [33]. In general, shorter execution times are desirable since they indicate faster performance and better efficiency of machine learning models or algorithms. 6. Receiver Operating Characteristic-Area Under Curve (ROC-AUC): It measures the ability of a binary classifier to distinguish between positive and negative classes across all possible threshold settings. It is widely used in machine learning to evaluate the performance of binary classification models, particularly in cases where the classes are imbalanced, or the cost of false positives and false negatives is different [34]. It ranges from 0 to 1, with higher values indicating better performance. The range 0.5 indicates  
Figure 6: Confusion matrix. that the classifier is performing at random, while the range 1 value indicates perfect classification. * Scalability: Scalability in machine learning refers to a model's ability to handle increasing amounts of data without requiring significant increases in processing time and resources [35]. It is crucial in applications involving large datasets, such as big data analytics, image recognition, natural language processing, and speech recognition. A scalable machine learning model can efficiently handle large datasets, making it practical for real-world use.