## 2 Methodology
### Proposed Meta-Learning Model
The development of the model was achieved through the utilization of the Keras library alongside the TensorFlow framework, employing Python version 3.10 for the programming tasks. To check the performance of the proposed meta-learning model, performance evaluation metrics such as accuracy and metrics for measuring errors such as mean square error (MSE), mean absolute error (MAE), and root MSE (RMSE), are used  
Figure 2: The scheme and implementation framework of the proposed system. for diabetes prediction and glucose level prediction, respectively, and are computed in Equations (2) and (3) [10; 16; 28; 29; 30; 31; 32; 33], as follows:  
\[\text{Accuracy}=100*\big{(}\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TP}+\text{ FN}+\text{TN}}\big{)} \tag{6}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}{\left(Y_{pred}-X_{ac}\right)^{2}}}{n}} \tag{7}\]  
where TP represents true positive, TN represents true negative, FP represents false positive, FN represents false negative, _Ypred_ represents estimated values, _Xac_ represents truth or actual values, and \(n\) is the number of total test data. Figure 2 illustrates the overall schematic and implementation framework of the proposed method, which is divided into three main phases: offline model training, online model testing, and model performance evaluation. In the offline training phase, the model is trained using preprocessed and normalized source input datasets from different tasks such as task 1, task 2...task n to ensure effective learning. Then, these different signals are given to pre-train models (model 1, model 2...model n) for pre-trained purposes. Then, the pre-trained model as previous knowledge is given to the meta-learner for fine-tuning purposes. Then, the online testing phase involves validating the well-trained model in real-time scenarios, crucial for assessing practical applicability. The fine-tuned meta-learning model uses the target data and the previous knowledge as input. The final phase, model performance evaluation, involves assessing the proposed meta-learning model's accuracy, efficiency, and robustness. This process includes calculating various evaluation metrics, implementing cross-validation techniques to ensure consistency, and continuously monitoring the model's performance. It also encompasses hyperparameter tuning to optimize the model, error analysis to understand its limitations, and comparing the model's performance against baselines or previous versions. The three phases of the proposed system are described in detail as follows: offline training phase: The denoised and preprocessed datasets from different tasks (task 1, task 2,..., task n). Normalize the data to ensure consistency across tasks. Then, use the prepared and denoised data to train pre-train models (model 1, model 2,..., model n). Once passed through the pre-training phase, the model learns and extracts features from the data and makes the meta-knowledge. This meta-knowledge serves as the foundation for the meta-learner, encapsulating task-specific knowledge. Then, use the pre-trained models as prior knowledge and feed them into a meta-learner. After completing pre-training, the knowledge gained from the pre-trained model is transferred for the fine-tuning process on target data by passing to the second phase. In the second phase, online testing phase: the meta-learner fine-tunes these models using additional small target data to generalize across tasks effectively. The final phase, model performance evaluation: indicates the finetuned meta-learning model results in diabetes prediction and glucose level measurement using measurement metrics of accuracy and error measurements by checking the actual and the predicted glucose level. Additionally, the model is validated on separate, unseen datasets to ensure it generalizes well to real-world data. Overall, this comprehensive evaluation ensures that the model maintains high accuracy and reliability.