ArticleIEEE journal of biomedical and health informatics2022
Gradient Boosting Machine and Efficient Combination of Features for Speech-Based Detection of COVID-19.
Article in IEEE journal of biomedical and health informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 59 citations in OpenAlex.
- Extracting cancer concepts from clinical notes using natural language processing: a systematic review.BMC bioinformatics · 2023Pooled it
- Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review.Journal of medical Internet research · 2023Pooled it
- Peritoneal cytology predicting distant metastasis in uterine carcinosarcoma: machine learning model development and validation.World journal of surgical oncology · 2025Article
- Ensemble machine learning models for predicting bone metastasis in bladder cancer.Frontiers in oncology · 2025Article
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- Assessment of Pepper Robot's Speech Recognition System through the Lens of Machine Learning.Biomimetics (Basel, Switzerland) · 2024Article
- Automated machine learning for predicting liver metastasis in patients with gastrointestinal stromal tumor: a SEER-based analysis.Scientific reports · 2024Article
- Biomedical semantic text summarizer.BMC bioinformatics · 2024Article
- Creation of a machine learning-based prognostic prediction model for various subtypes of laryngeal cancer.Scientific reports · 2024Article
- Understanding the impact of sisu on workforce and well-being: A machine learning-based analysis.Heliyon · 2024Article
- Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer.Frontiers in oncology · 2024Article
- Identification of disulfidptosis-related subtypes and development of a prognosis model based on stacking framework in renal clear cell carcinoma.Journal of cancer research and clinical oncology · 2023Article
- AUD-DSS: a decision support system for early detection of patients with alcohol use disorder.BMC bioinformatics · 2023Article
- An adaptive multi-modal hybrid model for classifying thyroid nodules by combining ultrasound and infrared thermal images.BMC bioinformatics · 2023Article
- Fitness dependent optimizer with neural networks for COVID-19 patients.Computer methods and programs in biomedicine update · 2023Article
- Prediction of mental effort derived from an automated vocal biomarker using machine learning in a large-scale remote sample.Frontiers in artificial intelligence · 2023Article
- Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism.Frontiers in pharmacology · 2023Article
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Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent times, speech-based automatic disease detection systems have shown several promising results in biomedical and life science applications, especially in the case of respiratory diseases. It provides a quick, cost-effective, reliable, and non-invasive potential alternative detection option for COVID-19 in the ongoing pandemic scenario since the subject's voice can be remotely recorded and sent for further analysis. The existing COVID-19 detection methods including RT-PCR, and chest X-ray tests are not only costlier but also require the involvement of a trained technician. The present paper proposes a novel speech-based respiratory disease detection scheme for COVID-19 and Asthma using the Gradient Boosting Machine-based classifier. From the recorded speech samples, the spectral, cepstral, and periodicity features, as well as spectral descriptors, are computed and then homogeneously fused to obtain relevant statistical features. These features are subsequently used as inputs to the Gradient Boosting Machine. The various performance matrices of the proposed model have been obtained using thirteen sound categories' speech data collected from more than 50 countries using five standard datasets for accurate diagnosis of respiratory diseases including COVID-19. The overall average accuracy achieved by the proposed model using the stratified k-fold cross-validation test is above 97%. The analysis of various performance matrices demonstrates that under the current pandemic scenario, the proposed COVID-19 detection scheme can be gainfully employed by physicians.
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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.