Abstract:
Chronic obstructive pulmonary disease (COPD) is a major worldwide health concern,
impacting millions worldwide by its progressive and debilitating effects. This study
aims to develop personalized machine learning models for accurate COPD risk
assessment, leveraging a comprehensive dataset that includes clinical history, lifestyle
factors, and environmental exposures. This project aims to improve the accuracy and
efficiency of COPD diagnosis and risk assessment worldwide by utilizing several
machine learning techniques, including Logistic Regression, Decision Tree, KNN,
Naive Bayes, and AdaBoost. The study involves systematic data preprocessing, feature
selection, and training of multiple algorithms to identify significant risk factors and
their interactions across diverse populations. The primary goal is to create risk
assessment tools that empower healthcare professionals and individuals to recognize
and mitigate COPD risks early. Expected outcomes include improved clinical decisionmaking, early intervention strategies, and better patient outcomes in COPD
management. Additionally, this research aims to make advanced diagnostic capabilities
accessible to underserved communities, aligning with global health initiatives to reduce
disparities and enhance healthcare equity. Through rigorous evaluation and
comparative analysis, the study aims to develop reliable and precise prediction models
that may be easily incorporated into real-life healthcare environments, thus
revolutionizing COPD management and diagnosis. This research not only enhances the
scientific comprehension of respiratory illness diagnoses but also has the potential to
greatly influence public health outcomes, healthcare accessibility, and the general wellbeing of varied communities worldwide.
