Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques

Abstract

Preventing exacerbation and seeking to determine the severity of the disease during the hospitalization of chronic obstructive pulmonary disease (COPD) patients is a crucial global initiative for chronic obstructive lung disease (GOLD); this option is available only for stable-phase patients. Recently, the assessment and prediction techniques that are used have been determined to be inadequate for acute exacerbation of chronic obstructive pulmonary disease patients. To magnify the monitoring and treatment of acute exacerbation COPD patients, we need to rely on the AI system, because traditional methods take a long time for the prognosis of the disease. Machine-learning techniques have shown the capacity to be effectively used in crucial healthcare applications. In this paper, we propose a voting ensemble classifier with 24 features to identify the severity of chronic obstructive pulmonary disease patients. In our study, we applied five machine-learning classifiers, namely random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), XGboost (XGB), and K-nearest neighbor (KNN). These classifiers were trained with a set of 24 features. After that, we combined their results with a soft voting ensemble (SVE) method. Consequently, we found performance measures with an accuracy of 91.0849%, a precision of 90.7725%, a recall of 91.3607%, an F-measure of 91.0656%, and an AUC score of 96.8656%, respectively. Our result shows that the SVE classifier with the proposed twenty-four features outperformed regular machine-learning-based methods for chronic obstructive pulmonary disease (COPD) patients. The SVE classifier helps respiratory physicians to estimate the severity of COPD patients in the early stage, consequently guiding the cure strategy and helps the prognosis of COPD patients.

Explainable Machine Learning Model for Predicting First-Time Acute Exacerbation in Patients with Chronic Obstructive Pulmonary Disease

“…A patient history of AECOPD is also not suitable for assessing the risk of first-time AECOPD, and some patient records may lack information regarding prior exacerbations. Hussain et al developed a GBM prediction model that excluded a history of AECOPD, and the model achieved high discrimination performance, with an AUC of 0.96 [19]. For the framework of the present study, we adopted ML-based modeling as the basis and then incorporated various clinical features using real-world data to account for local population characteristics, and our findings indicated that the GBM exhibited the highest prediction accuracy, with an AUC of 0.83.…”

Deep Learning-Based Prediction of Paresthesia after Third Molar Extraction: A Preliminary Study

“…In recent years, successful applications of CNNs in various fields have been reported. In the field of medical imaging, CNN architectures have shown efficient results similar to or better than those of human experts [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. In this study, we proved that a CNN architecture can accurately detect mandibular third molars and predict paresthesia before third molar extraction.…”

“…Deep learning has developed rapidly in recent years, making it possible to automatically extract information in the medical field, from diagnosis using medical imaging to analysis of activity and emotional patterns [ 1 , 2 , 3 ]. The deep convolutional neural network (CNN), a type of deep learning, has been widely applied to medical images due to its high performance in detection, classification, quantification, and segmentation [ 4 , 5 , 6 , 7 , 8 ].…”

Machine Learning Approaches for Predicting Acute Respiratory Failure, Ventilator Dependence, and Mortality in Chronic Obstructive Pulmonary Disease

“…The current study used common and important features to predict COPD prognosis. Further, the machine learning techniques that we established could provide physicians an opportunity to develop algorithms that integrated complex interaction factors to offer different possible prognoses to patients with COPD [ 38 ]. The addition of patient demographics, laboratory data, and comorbidities in this study to predict the possible outcomes of patients with COPD was successfully modeled.…”