###### Abstract  
The emergence of telemedicine as a transformative information technology paradigm has significantly impacted various industries worldwide, particularly healthcare. With the advancement of machine learning (ML), new avenues have opened for the diagnosis, management, and treatment of Chronic Obstructive Pulmonary Disease (COPD). This study aims to deepen the understanding of pulmonary data through advanced analytical techniques, enhanced diagnostic accuracy, and predicted disease progression. A comprehensive approach is proposed for assessing the severity of COPD using regression analysis and generalized linear models (GLM). The results indicate that the proposed model achieves a prediction accuracy of 86.7%, demonstrating its effectiveness in evaluating disease severity. This tool can assist healthcare professionals in making informed clinical decisions for the management of COPD. The findings underscore the potential of AI-driven technologies to improve patient outcomes in chronic disease management. m _Article_  
1 2025  
disease that significantly impairs quality of life and reduces life expectancy. It is recognized as a major global public health concern, with projections suggesting that by 2030, it will rank as the seventh most prevalent disease and the third leading cause of mortality worldwide. The increasing rates of smoking and worsening air quality in developing regions, particularly in Africa and Asia, are expected to intensify the COPD burden in these areas [10]. Machine learning algorithms offer a powerful means of detecting patterns in medical and diagnostic data that may indicate the presence or progression of COPD [11]. There is an urgent need to implement strategies that enable the early identification of individuals at high risk for developing COPD, as well as those already in the initial stages of the condition [12,13]. ML, a branch of AI, has emerged as a powerful tool for generating accurate predictions without the need for explicitly programmed instructions [14,15]. ML models utilize historical data to learn patterns and relationships that can be used to predict new outcomes. Generally, ML techniques are classified into three main categories: supervised learning, unsupervised learning, and reinforcement learning [16,17]. The machine learning pipeline typically begins with data exploration, followed by data preprocessing or cleansing, model development, and presentation of results [18]. Within the bioinformatics community, exploratory data analysis (EDA) has gained significant attention for its role in uncovering patterns in complex datasets. EDA is instrumental in formulating relevant research questions and generating testable hypotheses [19]. However, a major limitation in many existing studies is their reliance on small, narrowly defined datasets and a restricted set of features. This poses challenges to the generalizability of the models, as they may not perform reliably across diverse populations or varying data types [20]. Additionally, there is often a lack of validation using real-world data, which typically contains noise and variability not present in controlled research datasets. This raises concerns regarding the robustness of the models in practical clinical settings [21]. While ML-based algorithms have demonstrated considerable potential in the detection of COPD, further research is essential to enhance diagnostic precision, particularly in identifying disease severity at earlier stages. Although several studies have focused on predictive models for COPD prognosis, there remains a critical gap in the accurate prediction of disease severity across different stages. This study introduces a novel predictive framework for assessing COPD severity using GLMs. The proposed approach integrates a comprehensive set of clinically relevant covariates within a statistically rigorous and interpretable structure.