Enhancing COPD Diagnosis Accuracy: A Study on Regression Analysis and the Generalized Linear Model Technique
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Chronic Obstructive Pulmonary Disease (COPD) Research · Air Quality Monitoring and Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Intelligent Technologies Research Group, University of East London, University Way, London E16 2RD, UK
SK Research, Oxford Business College, Macclesfield House, New Road, Oxford OX1 1BY, UK
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