Enhancing COPD Diagnosis Accuracy: A Study on Regression Analysis and the Generalized Linear Model Technique
2025
Linah Saraireh | Hanaa Albanna | Mhd Saeed Sharif
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.
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