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

• 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Kardiologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Medical Engineering (hsv//eng)

Nyckelord

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

Publikations- och innehållstyp

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