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ValveHealthNet: a light deep learning model for accurate valvular heart disorder detection
IAES International Journal of Artificial Intelligence
ISSN 2252-8938, 2089-4872
Terbit 2026-06-01
Penerbit Institute of Advanced Engineering and Science
Bahasa eng
Abstrak
Valvular heart disease (VHD) is a significant global health issue, contributing to increased morbidity and mortality rates, particularly in aging populations. Current diagnostic methods, such as echocardiography and manual auscultation, face limitations in accessibility and accuracy, particularly in resource-constrained environments. This study introduces ValveHealthNet, a lightweight deep learning model designed to classify various VHDs using heart sound recordings. Leveraging a dataset of over 10,000 heart sounds, minimal preprocessing was applied by converting the audio signals into power spectra before feeding them into a convolutional neural network (CNN) combined with a bidirectional long short-term memory (BiLSTM) network. This model achieved impressive results, with an accuracy of 98% in training and testing and 98.4% through 10-fold cross-validation. This highly efficient model can be used in embedded systems, providing a cost-effective, AI-driven solution for early detection of VHD in settings where advanced diagnostic tools may be unavailable.
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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
<dc:title xml:lang="en-US">ValveHealthNet: a light deep learning model for accurate valvular heart disorder detection</dc:title>
<dc:creator>Alfraihat, Ausilah</dc:creator>
<dc:creator>Al-Sharu, Wafaa</dc:creator>
<dc:creator>Alqudah, Ali Mohammad</dc:creator>
<dc:subject xml:lang="en-US"></dc:subject>
<dc:subject xml:lang="en-US">Artificial intelligence; Convolutional neural networks; Deep learning; Embedded healthcare systems; Heart sound analysis; Heart valve; Heart valve diseases</dc:subject>
<dc:description xml:lang="en-US">Valvular heart disease (VHD) is a significant global health issue, contributing to increased morbidity and mortality rates, particularly in aging populations. Current diagnostic methods, such as echocardiography and manual auscultation, face limitations in accessibility and accuracy, particularly in resource-constrained environments. This study introduces ValveHealthNet, a lightweight deep learning model designed to classify various VHDs using heart sound recordings. Leveraging a dataset of over 10,000 heart sounds, minimal preprocessing was applied by converting the audio signals into power spectra before feeding them into a convolutional neural network (CNN) combined with a bidirectional long short-term memory (BiLSTM) network. This model achieved impressive results, with an accuracy of 98% in training and testing and 98.4% through 10-fold cross-validation. This highly efficient model can be used in embedded systems, providing a cost-effective, AI-driven solution for early detection of VHD in settings where advanced diagnostic tools may be unavailable.</dc:description>
<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
<dc:contributor xml:lang="en-US"></dc:contributor>
<dc:date>2026-06-01</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:type xml:lang="en-US"></dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/27546</dc:identifier>
<dc:identifier>10.11591/ijai.v15.i3.pp2643-2654</dc:identifier>
<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 15, No 3: June 2026; 2643-2654</dc:source>
<dc:source>2252-8938</dc:source>
<dc:source>2089-4872</dc:source>
<dc:source>10.11591/ijai.v15.i3</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/27546/15234</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2026 Ausilah Alfraihat, Wafaa Al-Sharu, Ali Mohammad Alqudah</dc:rights>
<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>
Sitasi dan pengindeksan
Jumlah menurut tiap indeks pada 13 Nov 2026; setiap indeks menghitung sitasi dengan cara berbeda.
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APA 7
Alfraihat, A., Al-Sharu, W., & Alqudah, A. M. (2026). ValveHealthNet: a light deep learning model for accurate valvular heart disorder detection. IAES International Journal of Artificial Intelligence, 15(3), 2643–2654. https://doi.org/10.11591/ijai.v15.i3.pp2643-2654
Salin
IEEE
A. Alfraihat, W. Al-Sharu, and A. M. Alqudah, “ValveHealthNet: a light deep learning model for accurate valvular heart disorder detection,” IAES International Journal of Artificial Intelligence, vol. 15, no. 3, pp. 2643–2654, 2026, doi: 10.11591/ijai.v15.i3.pp2643-2654.
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