Advancements in COPD Exacerbation Prediction and Personalized Care through Data Science

Abstract

This study focuses on predicting exacerbations of COPD (Chronic Obstructive Pulmonary Disease) using machine learning. COPD is a progressive illness that causes frequent and severe exacerbations, which contribute to the escalation of hospitalizations/deaths. The key factor in treating the patient and delivering care is predicting such exacerbations in a timely manner. Conventional practices for predicting exacerbations suffer from the drawback of identifying them late, typically when the symptoms have already become advanced. This paper addresses these weaknesses by constructing a predictive model based on Logistic Regression, Random Forest, and Gradient Boosting models, trained on the CDC Chronic Disease dataset, which contains more than one million records describing patient demographics, medical history, and environmental conditions. The models were evaluated based on four critical parameters: accuracy, recall, F1 score, and AUC-ROC. The Gradient Boosting model was found to be the best-performing model, with an accuracy of 87%, a recall of 79%, an F1 score of 83%, and an AUC-ROC value of 0.89, indicating that it has the greatest capability to predict COPD exacerbations. Random Forest and Logistic Regression had slightly lower overall accuracy and recall compared to the results shown by Jamann. The anticipated contributions of this work are to enhance the accuracy of predicting COPD exacerbations to a high level, allowing patients to receive personalized care through early intervention, and to provide a platform that can be integrated into clinical workflow. The unusual thing about the current study is the fact that ensemble learning models are included and different datasets included which will provide a hard and sound predictive model which can be put to use in the real-world clinical practices. This model has the potential to drive precision medicine, improve patient care, and cut the financial burden of COPD.