ArticleNeural computing & applications2021
Diagnosis of COVID-19 and non-COVID-19 patients by classifying only a single cough sound.
Article in Neural computing & applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 45 citations in OpenAlex.
- Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- The Use of Audio Signals for Detecting COVID-19: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Acoustic and Clinical Data Analysis of Vocal Recordings: Pandemic Insights and Lessons.Diagnostics (Basel, Switzerland) · 2024Article
- Cough Sounds in Screening and Diagnostics: A Scoping Review.The Laryngoscope · 2024Article
- TO-LAB model: Real time Touchless Lung Abnormality detection model using USRP based machine learning algorithm.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds.Expert systems with applications · 2023Article
- The voice of COVID-19: Breath and cough recording classification with temporal decision trees and random forests.Artificial intelligence in medicine · 2023Article
- A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms.Mobile networks and applications : MONET · 2023Article
- Computer-aided methods for combating Covid-19 in prevention, detection, and service provision approaches.Neural computing & applications · 2023Review
- Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method.Computers in biology and medicine · 2022Article
- Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study.Cognitive neurodynamics · 2022Article
- A systematic review on cough sound analysis for Covid-19 diagnosis and screening: is my cough sound COVID-19?PeerJ. Computer science · 2022Article
- Risk assessment in COVID-19 patients: A multiclass classification approach.Informatics in medicine unlocked · 2022Article
- Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the last month of 2019, a new virus emerged in China, spreading rapidly and affecting the whole world. This virus, which is called corona, is the most contagious type of virus that humanity has ever encountered. The virus has caused a huge crisis worldwide as it leads to severe infections and eventually death in humans. On March 11, 2020, it was announced by the World Health Organization that a COVID-19 outbreak has occurred. Computer-aided digital technologies, which eliminate many problems and provide convenience in people's lives, did not leave humanity alone in this regard and rushed to provide a solution for this unfortunate event. One of the important aspects in which computer-aided digital technologies can be effective is the diagnosis of the disease. Reverse transcription-polymerase chain reaction (RT-PCR), which is a standard and precise technique for diagnosing the disease, is an expensive and time-consuming method. Moreover, its availability is not the same all over the world. For this reason, it can be very attractive and important to distinguish the COVID-19 disease from a cold or flu through a cough sound analysis via smartphones which have entered into the lives of many people in recent years. In this study, we proposed a machine learning-based system to distinguish patients with COVID-19 from non-COVID-19 patients by analyzing only a single cough sound. Two different data sets were used, one accessible for the public and the other available on request. After combining the data sets, the features were obtained from the cough sounds using the mel-frequency cepstral coefficients (MFCCs) method, and then, they were classified with seven different machine learning classifiers. To determine the optimum values of hyperparameters for MFCCs and classifiers, the leave-one-out cross-validation (LOO-CV) strategy was implemented. Based on the results, the k-nearest neighbors classifier based on the Euclidean distance (
Indexed as
Identifiers
What 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.