A telemedicine tool framework for lung sounds classification using ensemble classifier algorithms

•It builds a telemedicine tool for predicting respiratory pathology using lung sounds.•The 3 approaches has compared to machine learning for the detection of lung sounds.•The 3 approaches are Improved Random Forest, AdaBoost, Gradient Boosting.•This tool has trained by Bagging & Boosting classif...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Measurement : journal of the International Measurement Confederation Ročník 162; s. 107883
Hlavní autori: Jaber, Mustafa Musa, Abd, Sura Khalil, Shakeel, P.Mohamed, Burhanuddin, M.A., Mohammed, Mohammed Abdulameer, Yussof, Salman
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Elsevier Ltd 01.10.2020
Elsevier Science Ltd
Predmet:
Acoustics
Algorithms
Classifiers
Ensemble learning
Feature extraction
Lung sounds
Lungs
Machine learning
Pathology
Pulmonary disorders
Respiratory diseases
Signal classification
Signal processing
Telemedicine
Lung sounds
Ensemble learning
Pulmonary disorders
Machine learning
ISSN:0263-2241, 1873-412X
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:•It builds a telemedicine tool for predicting respiratory pathology using lung sounds.•The 3 approaches has compared to machine learning for the detection of lung sounds.•The 3 approaches are Improved Random Forest, AdaBoost, Gradient Boosting.•This tool has trained by Bagging & Boosting classifiers with handcrafted features. Telemedicine is one of the medical services related to information exchange tools (eHealth). In recent years, the monitoring and classification of acoustic signals of respiratory-related disease is a significant characteristic in the pulmonary analysis. Lung sounds produce appropriate evidence related to pulmonary disorders, and to assess subjects pulmonary situations. However, this traditional method suffers from restrictions, such as if the doctor isn't very much practiced, this may lead to an incorrect analysis. The objective of this research work is to build a telemedicine framework to predict respiratory pathology using lung sound examination. In this paper, the three approaches has been compared to machine learning for the detection of lung sounds. The proposed telemedicine framework trained through Bagging and Boosting classifiers (Improved Random Forest, AdaBoost, Gradient Boosting algorithm) with an extracted set of handcrafted features. The experimental results demonstrated that the performance of Improved Random Forest was higher than Gradient Boosting and AdaBoost classifiers. The overall classification accuracy for the Improved Random Forest algorithm has 99.04%. The telemedicine framework was implemented with the Improved Random Forest algorithm. The telemedicine framework has achieved phenomenal performance in recognizing respiratory pathology.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2020.107883