Machine Learning for the Diagnosis of Chronic Obstructive Pulmonary Disease and Photoplethysmography Signal – Based Minimum Diagnosis Time Detection

2021 ◽  
pp. 42-58
Author(s):  
Engin Melekoğlu ◽  
Ümit Kocabıçak ◽  
Muhammed Kürşad Uçar ◽  
Mehmet Recep Bozkurt ◽  
Cahit Bilgin
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease

scholarly journals A machine learning approach to triaging patients with chronic obstructive pulmonary disease

PLoS ONE ◽  
2017 ◽  
Vol 12 (11) ◽  
pp. e0188532 ◽  
Author(s):  
Sumanth Swaminathan ◽  
Klajdi Qirko ◽  
Ted Smith ◽  
Ethan Corcoran ◽  
Nicholas G. Wysham ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Chronic Obstructive ◽  
Learning Approach ◽  
Obstructive Pulmonary Disease ◽  
Machine Learning Approach

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.

Diagnosing asthma and chronic obstructive pulmonary disease with machine learning

2017 ◽  
Vol 25 (3) ◽  
pp. 811-827 ◽  
Author(s):  
Dimitris Spathis ◽  
Panayiotis Vlamos
Keyword(s):  
Machine Learning ◽  
Chronic Obstructive Pulmonary Disease ◽  
Random Forest ◽  
Pulmonary Disease ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Forced Expiratory Volume ◽  
Random Forest Classifier ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease

This study examines the clinical decision support systems in healthcare, in particular about the prevention, diagnosis and treatment of respiratory diseases, such as Asthma and chronic obstructive pulmonary disease. The empirical pulmonology study of a representative sample (n = 132) attempts to identify the major factors that contribute to the diagnosis of these diseases. Machine learning results show that in chronic obstructive pulmonary disease’s case, Random Forest classifier outperforms other techniques with 97.7 per cent precision, while the most prominent attributes for diagnosis are smoking, forced expiratory volume 1, age and forced vital capacity. In asthma’s case, the best precision, 80.3 per cent, is achieved again with the Random Forest classifier, while the most prominent attribute is MEF2575.

scholarly journals Machine learning methods for diagnosis of chronic obstructive pulmonary disease in healthy subjects: Analysis of Risk factors To DEtect COPD (ARTDECO) (Preprint)

2020 ◽  
Author(s):  
Shigeo Muro ◽  
Masato Ishida ◽  
Yoshiharu Horie ◽  
Wataru Takeuchi ◽  
Shunki Nakagawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Chronic Obstructive Pulmonary Disease ◽  
Logistic Regression ◽  
Pulmonary Disease ◽  
Vital Capacity ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Copd Diagnosis

BACKGROUND Airflow limitation is a critical physiological feature in chronic obstructive pulmonary disease (COPD), for which long-term exposure to noxious substances including tobacco smoke is an established risk. However, not all long-term smokers develop COPD, meaning that other risk factors exist. OBJECTIVE To predict risk factors for COPD diagnosis using machine learning in an annual medical check-up database. METHODS In this retrospective, observational cohort study (Analysis of Risk factors To DEtect COPD [ARTDECO]), annual medical check-up records for all Hitachi Ltd. employees in Japan collected from April 1998 to March 2019 were analyzed. Employees who provided informed consent via an opt-out model were screened and those aged 30–75 years, without prior diagnosis of COPD, asthma, or history of cancer were included. The database included clinical measurements (e.g., pulmonary function tests) and questionnaire responses. To predict risk factors for COPD diagnosis within a 3-year period, the Gradient Boosting Decision Tree machine learning method (XGBoost) was applied as a primary approach, with logistic regression as a secondary method. A diagnosis of COPD was made when the ratio of the pre-bronchodilator forced expiratory volume in 1 second (FEV1) to pre-bronchodilator forced vital capacity (FVC) was <0.7 during two consecutive examinations. RESULTS Of the 26,101 individuals screened, 1,213 met the exclusion criteria and thus 24,815 individuals were included in the analysis. The top 10 predictors for COPD diagnosis were FEV1/FVC, smoking status, allergic symptoms, cough, pack years, hemoglobin A1c, serum albumin, mean corpuscular volume, percent predicted vital capacity value, and percent predicted value of FEV1. The area under the receiver operating characteristic curves of the XGBoost model and the logistic regression model were 0.956 and 0.943, respectively. CONCLUSIONS Using a machine learning model in this longitudinal database, we identified a set multiple of parameters as risk factors other than smoking exposure or lung function to support general practitioners and occupational health physicians to predict the development of COPD. Further research to confirm our results is warranted, as our analysis involved a database used only in Japan. CLINICALTRIAL Not applicable.

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.

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