## 1 Introduction
Filter methods, such as Chi-Square, Information Gain, and Gain Ratio, evaluate feature subsets based on their correlation with the target class, without relying on a specific learning algorithm. In contrast, wrapper methods use a learning algorithm in the evaluation process, generally producing more accurate results but requiring greater computational resources, especially for high-dimensional datasets. This computational burden requires innovative approaches to optimize the FS process. The performance of the wrapper methods also depends on the chosen learning algorithm [24]. Metaheuristic algorithms (MHs), inspired by natural phenomena, have demonstrated significant potential in addressing FS problems. These algorithms utilize dynamic search strategies to achieve a balance between exploration and exploitation, which makes them effective in navigating large search spaces [24]. Prominent MHs include Improved Dragonfly Algorithm (IDA), Binary Grey Wolf Optimizer (bGWO), Binary Harris Hawks Optimizer (BHHO), and Genetic Algorithm (GA). These algorithms have been successfully implemented in various optimization problems, including FS, where they efficiently identify subsets of near-optimal features [25; 26; 27; 28; 29; 30; 31]. In summary, while the current literature provides a robust foundation for understanding the potential of non-invasive BGL monitoring systems, there is a notable lack of studies that specifically employ advanced metaheuristic algorithms, such as IDA, in the FS process in this context. This research aims to bridge this gap by developing a robust model that enhances the accuracy of BGL estimation while reducing computational complexity. Through the integration of HRV features from PPG signals and systematic evaluation of model performance, the research aims to provide a feasible solution for non-invasive BGL monitoring, ultimately assisting individuals with diabetes in managing their condition. This study presents a novel methodology that integrates machine learning with metaheuristic algorithms for feature selection and the estimation of non-invasive BGL using PPG signals and HRV features. The main contributions of this work are summarized as follows:  
* Introduce a novel approach that utilizes PPG signals and HRV features to enable accurate non-invasive BGL measurement. * Develop a robust wrapper-based feature selection technique that employs metaheuristic algorithms, including IDA, bGWO, BHHO, and GA, to enhance model performance by selecting significant features and minimizing computational complexity. * Propose and evaluate a BGL estimation model that integrates IDA with Light Gradient Boosting Machine (LightGBM) alongside other regression models, including Random Forest (RF) and Extra Trees Regressor (ETR), demonstrating enhanced predictive accuracy and reliability for non-invasive glucose monitoring. The subsequent sections of this paper are organized as follows: Section 2 provides a comprehensive review of related works, including PPG-based glucose monitoring and FS methodologies. Section 3 covers the background. The proposed methodology, including data preparation, FS processes, and regression modeling, is detailed in Section 4. Section 5 presents the experimental setup, results, and discussion. Lastly, Section 6 concludes the study and highlights directions for future research.