Predictive modeling and risk assessment for copd using a machine learning approach

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.