## 1 Introduction  
Wearable sensors are interesting attention with great potential for biomedical applications and healthcare monitoring systems especially for chronic disease monitoring such as diabetes, cancer, heart diseases, and others [1]. Currently, usually used wearable devices include the detection of several biochemical and physical information, such as heart rate, blood glucose, and others [1, 2, 3]. Diabetes is a chronic condition characterized by abnormal blood glucose levels due to either ineffective utilization or insufficient production of insulin [4, 5]. It requires continuous monitoring because neglecting to do so can lead to the development of more complex health issues [5]. Also, poorly managed diabetes can lead to chronic damage to various organs including the kidneys, heart, and blood vessels. Furthermore, it increases the risk of serious conditions such as hypertension, stroke, and cardiovascular disease [3; 4; 5]. Furthermore, this unhealthy condition diminishes families, individuals, and national resources if left untreated. Therefore, there is a need for continuous monitoring, early prediction, and detection of chronic diseases such as diabetes, cancer, and others to avoid complications and save lives [4]. Moreover, the emergence of smart-computing sensors, such as smartphones, smart-watches, and other wearables, along with advancements in healthcare technology, has made it ever more convenient to continuously and remotely monitor patients' health statuses in both home and hospital settings [1]. Deploying artificial intelligence (AI) such as deep learning and machine learning algorithms in smart healthcare systems and the Internet of Medical Things (IoMT) infrastructure can further improve performance in terms of decision-making, data analysis, and achieving various constraints. Moreover, accurate detection of the r-peak plays a pivotal role in every aspect of electrocardiogram (ECG) application. However, current r-peak predictors face challenges such as long computational times, limited adaptability, and increased detection errors. These issues arise due to the non-stationarity of noise and the complex QRS morphology, which includes a downward deflection after the primary waves (Q), an upward deflection following the Q waves (R), and a downward deflection after the R wave (S). Factors like skin contact quality, signal processing errors, power supply fluctuations, user activities, and the materials of the wearable device can impact the accuracy of wearable sensor data detection. To address these issues, the combined meta-learning and DWT denoising technique are proposed. In recent years, various conventional deep learning and machine learning techniques [2; 3; 4; 5; 6; 7; 8; 9; 10; 11] have been developed to classify and detect r-peaks, and predict chronic diseases such as diabetes, cancer, and others. However, these methods face significant challenges. One of the primary issues is long computational time, which refers to the extensive processing duration required for model training and inference, often making real-time applications impractical. Fast learning problems, also known as convergence issues, occur when models struggle to efficiently reach optimal performance, resulting in prolonged training periods and suboptimal results. Existing methods also exhibit limited adaptability and flexibility, meaning they often fail to generalize well to new, unseen data or adapt to variations in input data [9]. This limitation can lead to higher prediction errors, reducing the reliability of these models in practical scenarios. A cohort study, detailed in [10], utilized ECG and glucose level sensors in conjunction with IGRNet for predicting diabetes. The study reported promising results, achieving an accuracy rate of 78.1% and an AUC (area under the curve) of 77.7%. Additionally, the model demonstrated efficient performance with a testing time of 101.2 s, highlighting its potential utility in real-time clinical applications.