## 3 Results and Discussion
Figure 5 shows the predicted glucose level by the proposed model result versus the actual glucose level. As shown in the figure, the x-axis indicates the actual glucose level whereas the y-axis indicates the predicted glucose level. The effectiveness of the proposed model is visually evident through the circles or points that closely align with the blue fitting line. The predicted and actual values are closely clustered around the ideal line, indicating the proposed method's accuracy. With a testing error represented by an RMSE of 0.010798, the proposed model achieves highly accurate predictions. Hence, the result indicates that the proposed meta-learning model would have a better prediction accuracy using only a small amount of data and low computational time. Based on the numerical analysis, the fine-tuned meta-learning improves the accuracy of glucose level prediction by 93.5631% compared with the traditional model without the pre-train model. Moreover, the model has been evaluated using receiver operating characteristics (ROC). As shown in Figure 6, the ROC curve represents the performance of meta-learning and without pre-train models. The y-axis represents the rate of true positives, whereas the x-axis shows the rate of false positives. The area under the curve (AUC) summarizes the ROC curve, serving as an indicator of model capacity to distinguish between different classes. A higher AUC value signifies superior model performance. Based on the demonstrated experimental result using meta-learning, the model reached an AUC of 0.99 or 99%, significantly outperforming the model without pre-training, which scored an AUC of 0.94 or 94%. Figure 7 demonstrates the detected r-peaks of the ECG signal. As demonstrated in the figure, the proposed method effectively detected the r-peaks, even when the r-peaks overlapped due to different reasons such as movement artifacts, poor electrode contact, electrical interference from nearby devices, or muscle activity. Shown in the figure are the detected r-peaks, QRS complex, and ECG signals. Hence, w  
Figure 5: Predicted vs actual glucose level using the proposed method. signals are overlapped, the model under consideration accurately measures the r-peaks of ECG signals. Moreover, the performance of the proposed meta-learning technique was also validated by comparing it with DWT denoising, without using DWT denoising, and without the pre-train model, based on accuracy evaluation metrics. Figure 8 shows the prediction results of meta-learning with DWT denoising, meta-learning without DWT denoising, and without pre-trained in terms of accuracy. The evaluation is based on accuracy metrics, providing a comprehensive analysis of how DWT denoising and pre-training influence the performance of meta-learning models. The results highlight the effectiveness of integrating DWT denoising in enhancing prediction accuracy, demonstrating a significant improvement over models that forgo this step. Additionally, the comparison underscores the importance of pre-training in the meta-learning framework, revealing its impact on achieving higher accuracy levels. The meta-learning model with the DWT denoising technique achieved an accuracy of 96.2%, outperforming both the model without DWT denoising, which achieved 85%, and the model without pre-training, which achieved 81% accuracy. The result indicates that DWT denoising and pre-training both play crucial roles in enhancing the accuracy of the meta-learning model. Based on the comprehensive numerical analysis, the proposed meta-learning diabetes detection model significantly enhances accuracy by 18.765% compared to a model without pre-training. This substantial  
Figure 6: ROC curve of the proposed model. Figure 7: Detected r-peaks using the proposed method. improvement in accuracy highlights the effectiveness of incorporating DWT denoising techniques and meta-learning techniques, demonstrating the superior performance of the pre-trained model.