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Machine Learning-Based Classification of Hypertension using CnD Features from Acceleration Photoplethysmography and Clinical Parameters

Authors:

Saad Abdullah, Abdelakram Hafid, Maria Lindén, Mia Folke, Annica Kristoffersson

Research group:

Biomedical Engineering

Publication Type:

Conference/Workshop Paper

Venue:

2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)

DOI:

10.1109/CBMS58004.2023.00344

Abstract

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.

Bibtex

@inproceedings{Abdullah6736,
author = {Saad Abdullah and Abdelakram Hafid and Maria Lind{\'e}n and Mia Folke and Annica Kristoffersson},
title = {Machine Learning-Based Classification of Hypertension using CnD Features from Acceleration Photoplethysmography and Clinical Parameters},
pages = {923--924},
month = {July},
year = {2023},
booktitle = {2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)},
url = {http://www.es.mdu.se/publications/6736-}
}