## 3 Results and Discussion  
This section provides an in-depth analysis of the findings derived from rigorous testing of innovative meta-learning techniques in conjunction with DWT de-noising methodologies, specifically tailored for advanced smart healthcare systems. These cutting-edge approaches have been applied to critical predictive tasks, such as the accurate forecasting of diabetes onset and the precise monitoring of glucose levels. The comprehensive evaluation demonstrates the potential of these novel techniques to significantly enhance the predictive capabilities of smart healthcare systems, thereby contributing to more effective and timely medical interventions for managing chronic conditions like diabetes. Figure 3 demonstrates the performance assessment of the proposed method concerning the impact of changing the iterations (k) steps. As shown in the figure, the RMSE and MSE of the proposed meta-learning model are decreased when the iterations (k) steps increase and vice versa. Furthermore, to evaluate the effectiveness of the proposed approach, this study compared the performance outcomes between models without pre-train and with pre-train meta-learning, to indicate the effect of pre-training, and the proposed meta-learning method is the state of art. Figure 4 shows the estimation error results in terms of the MAE depending on the number of iteration (k) steps in meta-learning and without pre-train. The meta-learning exhibits a notable trend of significantly reducing the MAE within just a few iterations (k) of steps compared with without pre-train. Therefore, these results indicate that the proposed meta-learning system rapidly adapts to the target's limited data by learning from previous experience and show the possibility of achieving better performance using both small limited data and few iterations. In addition, as illustrated in the figure, the fine-tuned meta-learning approach shows respectable progress and tends to decline compared to without pre-train. Nevertheless, the non-pre-trained model necessitates substantial investments in collecting extensive training data, computational resources, human effort, and time to achieve optimal performance, whereas the pre-trained meta-learning model is an economical, swift, and resource-lean solution tailored for the IoMT or smart healthcare application using wearable sensor systems. Moreover, by considering cost or time efficiency, comprehensive comparisons are conducted on the computational time to evaluate the performance of the proposed method. Figure 4: Comparison of performance of meta-learning, without pre-train, after fine-tune, and before fine-tune in terms of MAE and time. Figure 3: Proposed technique performance based on RMSE and MSE according to various iterations (k) steps. As illustrated in the accompanying figure, the computational time, both before and after the fine-tuning process, exhibited a significant increase in direct proportion to the rising number of iterations (K) steps. This trend underscores a clear and direct correlation between the number of iterative steps and the total time required for computation, highlighting the importance of optimizing the number of iterations to balance computational cost and performance efficiency. However, after the fine-tuning process is applied, there is a marked reduction in computational time, with a time value of 6 s and iteration of 24, when compared to the duration required before fine-tuning with a time of 20 s at the iteration of 24. This significant decrease in computational time post-fine-tuning demonstrates the efficiency improvements gained through the fine-tuning adjustments, thereby underscoring the importance of this step in optimizing the model's performance and operational efficiency. The meta-learning model proposed in this study achieved a low MAE of 0.00297 and validates notable achieved computational efficiency by completing tasks in less computational time and with fewer iterations (k) steps.