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Machine Learning-Based Classification of Hypertension using CnD Features from Acceleration Photoplethysmography and Clinical Parameters
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-4841-2488
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
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-8704-402X
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2023 (English)In: Proceedings - IEEE Symposium on Computer-Based Medical Systems, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 923-924Conference paper, Published paper (Refereed)
Abstract [en]

Cardiovascular diseases (CVDs) are a leading cause of death worldwide, and hypertension is a major risk factor for acquiring CVDs. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. In this study, a linear SVM machine learning model was used to classify subjects as normal or at different stages of hypertension. The features combined statistical parameters derived from the acceleration plethysmography waveforms and clinical parameters extracted from a publicly available dataset. The model achieved an overall accuracy of 87.50% on the validation dataset and 95.35% on the test dataset. The model's true positive rate and positive predictivity was high in all classes, indicating a high accuracy, and precision. This study represents the first attempt to classify cardiovascular conditions using a combination of acceleration photoplethysmogram (APG) features and clinical parameters The study demonstrates the potential of APG analysis as a valuable tool for early detection of hypertension.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2023. p. 923-924
Keywords [en]
acceleration photoplethysmography, cardiovascular, fiducial points, hypertension, PPG, Acceleration, Classification (of information), Learning systems, Statistical tests, Support vector machines, Cardiovascular disease, Causes of death, Clinical parameters, Machine-learning, Photoplethysmogram, Photoplethysmography
National Category
Cardiac and Cardiovascular Systems Medical Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-63964DOI: 10.1109/CBMS58004.2023.00344ISI: 001037777900162Scopus ID: 2-s2.0-85166469701ISBN: 9798350312249 (print)OAI: oai:DiVA.org:mdh-63964DiVA, id: diva2:1788354
Conference
36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023, Aquila, 22 June 2023 through 24 June 2023
Available from: 2023-08-16 Created: 2023-08-16 Last updated: 2023-10-25Bibliographically approved

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Abdullah, SaadAbdelakram, HafidLindén, MariaFolke, MiaKristoffersson, Annica

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