## 1 Introduction
Moreover, traditional machine learning models may achieve accuracy rates ranging from 68% to 74% in diabetes prediction on the Pima Indian dataset (PID) but often fall short when dealing with imbalanced datasets or noisy data [11]. The highest accuracy achieved so far was 95.1% using a combined CNN-LSTM model [11]. To address these issues, the recent deep learning method was proposed for handling complex data processing and imbalanced data compared to traditional machine learning [9; 11]. Nevertheless, achieving improved predictive accuracy and improving the generalization performance of using deep learning methods entails a large amount of training data and training time. However, collecting sufficient amounts of wearable sensor data is complex, time-consuming, and costly. Moreover, real-time data collection for chronic diseases such as diabetes, cancer, cardiovascular diseases, and others is not only time-consuming and tedious but also resource-intensive due to the constant need for continuous or frequent monitoring across various sensors and devices [12; 13; 14; 15; 16; 17]. Therefore, to address the problem of requiring a huge amount of training data and to enable the application of AI in data-scarce scenarios, a meta-learning approach is proposed. This approach reduces the total data required for training by allowing AI to adapt from experience or previous knowledge. The presence of noise in wearable sensor data significantly compromises the performance of the proposed meta-learning model. To address this issue, employing denoising techniques emerges as a viable solution, enhancing the model's accuracy and reliability. Moreover, effective and powerful techniques for denoising signals are significant for wearable sensors, devices, and others [18; 19]. Recently, Various denoising and noise algorithms have been proposed, such as independent component analysis (ICA), adaptive filters, adaptive Fourier decomposition, empirical mode decomposition (EMD), median filter, Savitzky-Golay filter, moving average filter, Kalman filters, Bayesian filter framework, wavelet technique, clustering of morphological features, and neural networks [19; 20]. However, DWT denoising stands out as equally effective, and superior, compared to these proposed techniques [19; 20]. Therefore, the DWT denoising algorithm is implemented in the proposed system. In this study, meta-learning and DWT are proposed and demonstrated for diabetes prediction systems, to increase adaptability and learnability, and improve prediction accuracy. The proposed method can reduce the amount of wearable biomedical sensor data needed to train predictive models in the target domain of interest needed for training by leveraging prior knowledge or experiences. Therefore, meta-learning and DWT denoising techniques are applied to enhance the prediction accuracy of smart health systems. The experimental results verify that the proposed technique enhanced the prediction accuracy. The study seeks to make the following contributions:  
* The proposed meta-learning method is appropriate to empower AI, since biomedical wearable sensor data collection is expensive, challenging, or inadequate. * An advanced multi-level denoising technique has been implemented and demonstrated for smoothing the noise signals and improving the performance of the proposed meta-learning model. * The proposed meta-learning system significantly improves disease detection accuracy and efficiency while enabling adaptation to new data and learning from limited samples. It streamlines the training of predictive models for health wearables, enhancing efficiency and adaptability to new health tasks or patient conditions by leveraging diverse source datasets. * After conducting comprehensive experiments and comparative analyses, meta-learning demonstrated significant outperformance compared to traditional methods without pre-training. This leads to a marked improvement in diabetes detection performance in healthcare monitoring systems. Further, the remaining parts of this study are described as follows.