## 1 Introduction  
Diabetes is a chronic medical condition characterized by inadequate insulin production by the pancreas or the body's inability to effectively utilize insulin. This disorder is associated with persistent hyperglycemia, which can lead to severe complications, including cardiovascular disease, renal failure, neuropathy, and lower-limb amputations [1]. In 2021, over 537 million adults were diagnosed with diabetes, with projections indicating an increase to 783 million by 2045. Beyond the human impact, the economic burden of diabetes is significant, with global expenditure surpassing 5955 billion annually [2]. Accurate monitoring of blood glucose levels (BGLs) is essential in the management of diabetes and in the prevention of complications associated with the disease. Conventional methods, such as clinical laboratory tests and glucose meters (glucometers), are characterized by invasive procedures that require blood samples, typically obtained from the patient's finger. Although these methods are reliable, they are invasive, inconvenient, and unsuitable for continuous monitoring [3]. Consequently, there is a pressing demand for non-invasive, precise, and cost-effective alternatives for glucose monitoring. Previous studies have demonstrated photoplethysmography (PPG) as a promising non-invasive technique for monitoring BGLs [4; 5; 6; 7; 8; 9; 10; 11; 12]. PPG is an optical technique that measures variations in blood volume by analyzing changes in light intensity as it passes through or reflects off the tissue. The simplicity, low cost, and ease of integration into wearable devices make PPG an attractive candidate for continuous glucose monitoring. Variations in BGL alter the optical properties of hemoglobin, affecting light absorption and scattering, which can be detected through PPG waveforms. Moreover, heart rate variability (HRV) metrics derived from PPG signals provide additional information on glucose fluctuations by reflecting autonomic nervous system activity [13; 14; 15; 16; 17; 18; 19; 20; 21]. Figure 1 shows how the PPG signal is acquired using a finger sensor equipped with a photodiode to capture infrared LED light. By deriving the absorbance related to blood glucose from the AC component of the PPG wave, the BGL is calculated as follows [23]:  
\[\triangle\mathrm{OD}_{\lambda}=\log\bigg{(}1+\frac{\triangle I_{\lambda}(t_{ i})}{I_{\lambda}(t_{i+1})}\bigg{)}. \tag{1}\]  
The difference \(\Delta\mathrm{OD}_{\lambda}\) represents the variance in optical densities at time \(t_{i}\) and \(t_{i+1}\), while \(\Delta I_{\lambda}(t_{i})\) denotes the pulsatile element at time \(t_{i}\), and I(\(t_{i}+1\)) indicates the light intensity at time \(t_{i+1}\). Despite its significant potential, the efficient application of PPG for glucose monitoring encounters substantial challenges. PPG signals are inherently high-dimensional, encompassing a variety of derived features, such as waveform attributes and HRV metrics. This increased dimensionality can lead to redundancy and noise, reducing the accuracy of predictive models. Previous studies have explored advanced machine learning (ML) approaches, including convolutional neural networks (CNN) and support vector regression (SVR), to analyze PPG data. However, these methods frequently employ complex algorithms and require large datasets, which limit their feasibility in non-clinical environments [4; 5; 6; 7; 8; 9; 10; 11; 12]. This highlights the need for preprocessing strategies to address these limitations effectively. Feature selection (FS) is a vital preprocessing phase in ML-based systems, addressing these challenges by reducing dimensionality and selecting the most informative features. Figure 1: PPG measurement process [22]. FS methods can be classified based on two main factors: evaluation criteria and search strategies. In terms of evaluation, FS methods are generally categorized as filter or wrapper techniques.