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<p>Chronic Obstructive Pulmonary Disease (COPD) is a progressive lung condition that causes restricted airflow and breathing problems in patients. The disease has remained a leading cause of mortality worldwide, yet early prediction of at-risk individuals remains a challenge. Traditional diagnostic approaches rely on symptomatic assessment or expensive, inaccessible clinical tests rather than predictive modeling, which delays disease intervention. This study explores the potential of machine learning in predicting COPD risk by utilizing the extensive <i>All of Us</i> database, which provides diverse health data. Using a cohort of 42,941 individuals, we extracted demographic, lifestyle, and clinical features that are relevant to COPD susceptibility in the literature. Extensive data processing techniques were utilized that involved handling missing values, feature selection, and normalization. Feature importance analysis highlighted age,&#xa0;smoking history, and comorbidities as key contributors to COPD risk. Various machine learning algorithms, including random forest, multi-layer perceptron, and support vector machine, were trained and validated to assess the predictive performance of our framework. Performance evaluation based on accuracy, area under the receiver operating characteristic curve (AUC-ROC), and area under the precision-recall curve (AUC-PR) metrics indicates the random forest model achieved the strongest overall performance, with an accuracy of 83%, AUC-ROC of 0.89, and AUC-PR of 0.78. While some prior studies report higher AUC-ROC, those often rely on specialized data (e.g., imaging, genetic, or questionnaire-based inputs) and small or imbalanced datasets. In contrast, our model achieves competitive performance using a reduced, accessible clinical feature set across a large, diverse cohort. Our findings suggest that machine learning-based predictive models can greatly enhance the early identification of at-risk individuals to allow targeted interventions if needed. By integrating such predictive analytics into healthcare systems, we hope to shift focus to more proactive risk mitigation in COPD care.</p>

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Prediction of chronic obstructive pulmonary disease using machine learning models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月1日

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  • Sher Ali,
  • Omair Faqah,
  • Elise Neubarth,
  • Mohammad Shehroz Ashraf,
  • Michael DeGiorgio,
  • Mark Block,
  • Waseem Asghar

摘要

Chronic Obstructive Pulmonary Disease (COPD) is a progressive lung condition that causes restricted airflow and breathing problems in patients. The disease has remained a leading cause of mortality worldwide, yet early prediction of at-risk individuals remains a challenge. Traditional diagnostic approaches rely on symptomatic assessment or expensive, inaccessible clinical tests rather than predictive modeling, which delays disease intervention. This study explores the potential of machine learning in predicting COPD risk by utilizing the extensive All of Us database, which provides diverse health data. Using a cohort of 42,941 individuals, we extracted demographic, lifestyle, and clinical features that are relevant to COPD susceptibility in the literature. Extensive data processing techniques were utilized that involved handling missing values, feature selection, and normalization. Feature importance analysis highlighted age, smoking history, and comorbidities as key contributors to COPD risk. Various machine learning algorithms, including random forest, multi-layer perceptron, and support vector machine, were trained and validated to assess the predictive performance of our framework. Performance evaluation based on accuracy, area under the receiver operating characteristic curve (AUC-ROC), and area under the precision-recall curve (AUC-PR) metrics indicates the random forest model achieved the strongest overall performance, with an accuracy of 83%, AUC-ROC of 0.89, and AUC-PR of 0.78. While some prior studies report higher AUC-ROC, those often rely on specialized data (e.g., imaging, genetic, or questionnaire-based inputs) and small or imbalanced datasets. In contrast, our model achieves competitive performance using a reduced, accessible clinical feature set across a large, diverse cohort. Our findings suggest that machine learning-based predictive models can greatly enhance the early identification of at-risk individuals to allow targeted interventions if needed. By integrating such predictive analytics into healthcare systems, we hope to shift focus to more proactive risk mitigation in COPD care.