## 2 Methodology
### Proposed Meta-Learning Model  
This section provides a detailed explanation of the proposed meta-learning model, as illustrated in Figure 2. A remarkable trait of human intelligence is its capacity to use existing knowledge efficiently to speed up the learning of new tasks. To emulate this human-like skill within artificial agents, researchers have introduced and extensively studied the concept of meta-learning. This approach holds promise for pioneering learning and adaptation within situations where only a few or limited data are available [16; 17]. Like human learning methods, a model possesses the ability to acquire new tasks by leveraging experience gained from familiar and related ones [12; 13; 14; 15; 16]. The meta-learning technique can swiftly learn a new task from a limited amount of data in the target domain [12; 16; 17]. Using meta-learning shows great potential to decrease the amount of biomedical data needed to train predictive models in the target domain of interest [12; 16]. Deep learning methods have enabled us to effectively process unlabeled data such as sensor data, medical images, medical health text records, audio, and others [24; 25; 26; 27]. However, improving performance with deep learning requires extensive data. Moreover, meta-learning, as proposed, has the potential to transform machine learning by helping models learn and adjust to new tasks and environments quickly and efficiently, by reducing the huge amounts of training data and learning from past experiences similar to human beings. Figure 1: Proposed discrete wavelet transform (DWT) multi-level denoising (**a**) before applying the denoising technique, (**b**) after applying the denoising technique. As shown, Figure 2 demonstrates the overall schematic and implementation framwork of the proposed method. As shown in the figure, the large data or source data with different patients (different ECG and glucose sensors signals) as diverse tasks, such as task 1, task 2, and task n, provides for the pre-train model, such as meta-model 1, meta-model 2...meta-model n, for knowledge transfer. In addition, the proposed system can support different related wearable sensors such as pressure sensors, glucose sensors, motion sensors, and others. Moreover, meta-learning has significant potential to decrease the quantity of biomedical data required for training predictive models in the specific target domain and using related data as source data for pre-training [12; 16; 17]. Meta-learning offers a solution for chronic diseases, where acquiring extensive training data and implementing AI can be challenging. By leveraging data sets from other related diseases, institutes, or cell types, a model can be pre-trained to be ready for learning new tasks even before encountering data in the target domain [12]. After completing the pre-training phase, the model acquires the ability to learn and extract features from the data to make meta knowledge. The model's weight is fine-tuned to optimize a particular objective such as iteration steps (k), learning rate, and others. The learning rate, threshold, and iteration steps (k) of the proposed model are set to 0.001, 02, and 24, respectively. After completing all the steps of pre-training stages, the knowledge gained from the pre-trained model is shared for the fine-tuning process on a few examples. Following the fine-tuning stage, the fine-tuned proposed model is tested using new unseen test data. Finally, a fine-tuned meta-learning model can forecast diabetes and glucose levels. The evaluation of both the proposed prediction model and denoising model was conducted on a computer system specifically configured with an Intel(R) Core (TM) i5-8500T processor (Taipei, Taiwan), which operates at a clock speed of 2.10 GHz, ensuring robust processing capabilities. This setup was further enhanced with 8 GB of RAM, providing adequate memory resources to efficiently manage the computational load required for executing the model, thus guaranteeing a smooth and effective performance evaluation process.