Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease

2020 ◽  
Vol 188 ◽  
pp. 105267 ◽  
Author(s):  
Chenshuo Wang ◽  
Xianxiang Chen ◽  
Lidong Du ◽  
Qingyuan Zhan ◽  
Ting Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Acute Exacerbations

Prediction of chronic obstructive pulmonary disease stages using machine learning algorithms

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Predictive Value ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Chronic Obstructive ◽  
Optimal Prediction ◽  
Obstructive Pulmonary Disease ◽  
Disease Stages

Identifying chronic obstructive pulmonary disease (COPD) severity stages is of great importance to control the related mortality rates and reduce the associated costs. This study aims to build prediction models for COPD stages and, to compare the relative performance of five machine learning algorithms to determine the optimal prediction algorithm. This research is based on data collected from a private hospital in Egypt for the two calendar years 2018 and 2019. Five machine learning algorithms were used for the comparison. The F1 score, specificity, sensitivity, accuracy, positive predictive value and negative predictive value were the performance measures used for algorithms comparison. Analysis included 211 patients’ records. Our results show that the best performing algorithm in most of the disease stages is the PNN with the optimal prediction accuracy and hence it can be considered as a powerful prediction tool used by decision makers in predicting severity stages of COPD.

scholarly journals The Potential of Machine Learning Algorithms in Discriminating Chronic Obstructive Pulmonary Disease and Healthy Saliva Samples

2021 ◽  
Vol 10 (4) ◽  
pp. 155-163
Author(s):  
Atefeh Goshvarpour ◽  
Ateke Goshvarpour
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Intelligent System ◽  
Smoking Status ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Age Range

Background: Today, with the spread of tobacco use and increased environmental pollutions, respiratory diseases are considered important factors threatening human life. Chronic obstructive pulmonary disease (COPD) is a kind of inflammatory lung disease. Clinically, COPD is currently diagnosed and monitored by spirometry as the gold-standard technique although spirometry systems encounter some limitations. Thanks to the economical handling and sampling, practicality, and non-invasiveness of saliva biomarkers, it is promising for the testing environment. Accordingly, the current analytic observational study aimed to propose an intelligent system for COPD detection. Materials and Methods: To this end, 40 COPD (8 females and 32 males in the age range of 71.67±8.27 years) and 40 controls (17 females and 23 males within the age range of 38.23±14.05 years) were considered in this study. The samples were characterized by absolute minimum value and the average value of the real and imaginary parts of saliva permittivity. Additionally, the age, gender, and smoking status of the participants were determined, and then the performance of various classifiers was evaluated by adjusting k in k-fold cross-validation (CV) and classifier parameterization. Results: The results showed that the k-nearest neighbor outperformed other classifiers. Using both 8- and 10-fold CV, the maximum classification rates of 100% were achieved for all k values. On the other hand, increasing the k in k-fold CV improved classification performances. The positive role of parameterization was revealed as well. Conclusions: Overall, these findings authenticated the potential of machine learning (ML) algorithms in the diagnosis of COPD using subjects’ saliva features and demographic information.

scholarly journals Chronic Obstructive Pulmonary Disease with Acute Exacerbation Prediction System Using Wearable Device and Machine Learning and Deep Learning (Preprint)

2020 ◽  
Author(s):  
Guo-Hung Li ◽  
Chia-Tung Wu ◽  
Chun-Ta Huang ◽  
Feipei Lai ◽  
Lu-Cheng Kuo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
Air Quality ◽  
Pulmonary Disease ◽  
Smartphone Application ◽  
Wearable Devices ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Copd Patients ◽  
Acute Exacerbations

BACKGROUND World Health Organization anticipated that by 2030, chronic obstructive pulmonary disease (COPD) will be the third leading cause of mortality and the seventh leading cause of morbidity worldwide. Acute exacerbations of chronic obstructive pulmonary disease (AE-COPD) are associated with accelerated decline in lung function, diminished quality of life, and higher mortality. Accurate early detection of acute exacerbations will enable early management and reduce mortality. OBJECTIVE To develop a prediction model of AE-COPD using lifestyle data, environment factors and patient’s symptoms to achieve early detection of AE-COPD in the forthcoming 7 days. METHODS This prospective study was conducted in National Taiwan University Hospital. COPD patients without pacemaker and pregnancy were invited for enrollment. Lifestyle, temperature, humidity and fine particulate matter (PM2.5) were collected using wearable devices, home air quality sensing devices, and smartphone application. The episodes of AE-COPD were evaluated by standardized questionnaires. With these input features, we evaluated the prediction performance of machine learning models with random forest, decision tree, kNN, linear discriminant analysis, AdaBoost, and a deep neural network model. RESULTS The continuous real-time monitoring of lifestyle and indoor environment factors were implemented in this study by integrating home air quality sensing devices, smartphone applications, and wearable devices. All data from 67 COPD patients were collected prospectively during a mean of 4-month follow-up and 25 episodes of AE-COPD were detected. For prediction of AE-COPD within the next 7 days, our AE-COPD predictive model had accuracy of 92.1%, sensitivity of 94%, and specificity of 90.4%. The receiver operating characteristic curve analysis showed the area under the curve of the model in predicting AE-COPD was >0.9. The most weighting variables in the model were daily walking steps, climbing stairs and daily moving distances. CONCLUSIONS Using wearable devices, home air quality sensing devices, smartphone application and supervised prediction algorithms, we achieved an excellent predictive power for the task of predicting whether a patient will experience an acute exacerbation of COPD within the next 7 days. The system was capable of making reliable predictions with enough time in advance when a patient is going to have an AE-COPD.

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