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A Decision Support System for Cardiac Disease Diagnosis Based on Machine Learning Methods
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-1940-1747
Department of Biomedical Engineering, Linköping University, Sweden.
2017 (English)In: Studies in Health Technology and Informatics, ISSN 0926-9630, Vol. 235, p. 43-47Article in journal (Refereed) Published
Abstract [en]

This paper proposes a decision support system for screening pediatric cardiac disease in primary healthcare centres relying on the heart sound time series analysis. The proposed system employs our processing method which is based on the hidden Markov model for extracting appropriate information from the time series. The binary output resulting from the method is discriminative for the two classes of time series existing in our databank, corresponding to the children with heart disease and the healthy ones. A total 90 children referrals to a university hospital, constituting of 55 healthy and 35 children with congenital heart disease, were enrolled into the study after obtaining the informed consent. Accuracy and sensitivity of the method was estimated to be 86.4% and 85.6%, respectively, showing a superior performance than what a paediatric cardiologist could achieve performing auscultation. The method can be easily implemented using mobile and web technology to develop an easy-To-use tool for paediatric cardiac disease diagnosis. © 2017 European Federation for Medical Informatics (EFMI) and IOS Press.

Place, publisher, year, edition, pages
IOS Press , 2017. Vol. 235, p. 43-47
Keywords [en]
congenital heart disease screening, decision support system, heart sound, Hidden Markov model
National Category
Health Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-35313DOI: 10.3233/978-1-61499-753-5-43Scopus ID: 2-s2.0-85018802241ISBN: 9781614997528 (print)OAI: oai:DiVA.org:mdh-35313DiVA, id: diva2:1095202
Available from: 2017-05-12 Created: 2017-05-12 Last updated: 2022-04-22Bibliographically approved

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Lindén, Maria

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