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Enhancing Heart Murmur Detection: A Comparative Study of Machine Learning Models Utilizing Digital Stethoscopes
Centre for Applied AI (CAAI) Sheridan College Oakville, Ontario, Canada.
Sheridan College (Oakville) · Faculty of Applied Science and Technology, Canada .
School of Computer Science University of Guelph Guelph, Ontario, Canada.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-4841-2488
2024 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Machine learning has proven to be a powerful tool across many domains including healthcare. Heart sound classification using machine learning can revolutionize cardiac care by improving diagnostic accuracy, enabling early intervention, and facilitating personalized treatment strategies. However, obtaining labeled data for classification model training can be difficult, especially for rare or complex conditions. Furthermore, classifying heart sounds accurately can be challenging due to the variabilityin sound patterns and the presence of noise which requires preprocessing. In this paper, two machine learning models were trained and evaluated using DenseNet architecture on the CirCorDigiScope Phonocardiagram dataset and an ensemble of SupportVector Machine (SVM) and Decision Tree (DT) algorithms on acustom dataset curated by our partner, Tech4Life. The F1 score of the CirCor trained model was 75%. This is our effort to advance the application of machine learning in heart sound classification.

Place, publisher, year, edition, pages
2024. p. 123-129
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-68982DOI: 10.1109/AIC61668.2024.10731067Scopus ID: 2-s2.0-85210266737ISBN: 9798350384598 (print)OAI: oai:DiVA.org:mdh-68982DiVA, id: diva2:1912300
Conference
2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC)
Available from: 2024-11-11 Created: 2024-11-11 Last updated: 2024-12-04Bibliographically approved

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Abdullah, Saad

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