## 2 Related Literature Review  
The adoption and integration of telehealth into healthcare delivery systems hold significant potential to improve patient outcomes and reduce treatment-related costs. The continued expansion of telehealth services is largely driven by advancements in technology that allow healthcare providers to connect with patients effectively and empathetically, regardless of geographic location [22,23]. In the context of machine learning, various studies have outlined standard workflows that generally include steps such as data preparation, model development and training, model evaluation, selection, and deployment [24,25]. These workflows provide a structured approach to building predictive models for healthcare applications, including the diagnosis and monitoring of chronic respiratory conditions like COPD. Recent advances in machine learning have introduced sophisticated approaches such as reinforcement learning and multi-modal learning, which have demonstrated promising results in complex diagnostic tasks. For instance, [PERSON] et al. developed an energy-efficient and damage-recovery slithering gait for snake-like robots based on reinforcement learning and inverse reinforcement learning [26]. Similarly, [PERSON] et al. proposed a multi-modal self-paced locality-preserving learning framework aimed at improving [PERSON]'s disease diagnosis, showcasing the integration of diverse data modalities and advanced learning strategies [27]. Furthermore, [PERSON] et al. investigated learning-based refinement of object boundaries, offering insights that could enhance feature extraction and imaging analysis in medical applications [28]. Incorporating such methodologies in future COPD research may facilitate models capable of capturing complex nonlinear patterns and multi-source data, ultimately improving diagnostic accuracy and clinical relevance. One study utilized Random Forests, an ensemble learning algorithm, to predict the percentage of forced expiratory volume in one second (FEV1%) using lung function test data and patient demographic information. The model demonstrated strong predictive performance and effectively identified individuals with airflow obstruction [29]. Another study applied gradient boosting, and another applied the ensemble learning technique, to estimate the severity of airflow obstruction. By leveraging patient demographics and pulmonary function test results, the model was able to accurately classify patients across different stages of COPD [30]. A third study employed artificial neural networks, a deep learning approach, to assess the extent of airflow obstruction using spirometry data. The model also achieved high predictive accuracy and successfully identified varying degrees of COPD severity [1]. Regression algorithms have proven particularly effective in COPD prediction tasks, as they are capable of modeling continuous outcome variables such as the degree of airflow obstruction based on multiple predictor variables, including demographic information, smoking history, and lung function test results [31]. One study applied linear regression to forecast FEV1 values using these features and reported strong predictive accuracy, enabling the identification of patients at heightened risk of developing COPD. In the same study, multiple linear regression was also employed, yielding similarly robust results in predicting airflow obstruction [32]. Several studies have employed different machine learning approaches to improve COPD detection. One study utilized logistic regression to predict the presence or absence of COPD based on patient demographics and lung function test results, reporting satisfactory diagnostic accuracy [33]. Another investigation applied a deep learning algorithm to classify computed tomography (CT) images as normal or indicative of COPD, achieving an accuracy of 88.9% in detecting the disease from imaging data [34]. Additionally, [PERSON] et al.