ArticleJournal of medical systems2019
Respiratory Sound Based Classification of Chronic Obstructive Pulmonary Disease: a Risk Stratification Approach in Machine Learning Paradigm.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Artificial Intelligence Applications in Chronic Obstructive Pulmonary Disease: A Global Scoping Review of Diagnostic, Symptom-Based, and Outcome Prediction Approaches.Biomedicines · 2025Review
- Mapping respiratory health digital interventions in South and Southeast Asia: a scoping review.Journal of global health · 2025Article
- Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals.Diagnostics (Basel, Switzerland) · 2025Article
- Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease.Scientific reports · 2025Observational
- Automated system for diagnosing pulmonary fibrosis using crackle analysis in recorded lung sounds based on iterative envelope mean fractal dimension filter.Physiological measurement · 2025Article
- Evaluation of Deep Learning Methods for Pulmonary Disease Classification.Current medical imaging · 2025Article
- Using machine learning for early detection of chronic obstructive pulmonary disease: a narrative review.Respiratory research · 2024Review
- Review
- Breathing sounds analysis system for early detection of airway problems in patients with a tracheostomy tube.Scientific reports · 2023Article
- Deep learning-based lung sound analysis for intelligent stethoscope.Military Medical Research · 2023Review
- Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview.Diagnostics (Basel, Switzerland) · 2023Review
- An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features.Diagnostics (Basel, Switzerland) · 2023Article
- Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review.Computers in biology and medicine · 2023Review
- Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- A review on lung disease recognition by acoustic signal analysis with deep learning networks.Journal of big data · 2023Article
- A random subspace ensemble classification model for discrimination of power quality events in solar PV microgrid power network.PloS one · 2022Article
- Acquisition and Classification of Lung Sounds for Improving the Efficacy of Auscultation Diagnosis of Pulmonary Diseases.Medical devices (Auckland, N.Z.) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article investigates the classification of normal and COPD subjects on the basis of respiratory sound analysis using machine learning techniques. Thirty COPD and 25 healthy subject data are recorded. Total of 39 lung sound features and 3 spirometry features are extracted and evaluated. Various parametric and nonparametric tests are conducted to evaluate the relevance of extracted features. Classifiers such as support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), decision tree and discriminant analysis (DA) are used to categorize normal and COPD breath sounds. Classification based on spirometry parameters as well as respiratory sound parameters are assessed. Maximum classification accuracy of 83.6% is achieved by the SVM classifier while using the most relevant lung sound parameters i.e. median frequency and linear predictive coefficients. Further, SVM classifier and LR classifier achieved classification accuracy of 100% when relevant lung sound parameters, i.e. median frequency and linear predictive coefficient are combined with the spirometry parameters, i.e. forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV
Indexed as
Identifiers
31254141What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.