Predicting Vascular Age using PPG Signals and Machine Learning Algorithms: A Non-Invasive Approach for Early Cardiovascular Risk Detection

Cardiovascular diseases (CVDs) pose a significant global health challenge, necessitating effective strategies for early risk assessment and intervention. Arterial stiffness, a critical parameter within the cardiovascular system, is considered a primary risk factor for CVD development. This parameter undergoes natural age-related structural and functional changes in arterial walls, resulting in increased arterial stiffness and reduced elasticity. These changes contribute to vascular aging and alter pulse wave velocity. Photoplethysmography (PPG) offers a non-invasive means to assess these alterations. Our research leverages PPG in combination with machine learning to develop a portable, non-invasive method for predicting vascular age. We identify and select 21 features from the fiducial points of the digital artery PPG signals obtained from simulated pulse waves across a diverse sample of healthy adults aged 25 to 75 years, its first derivative, and its second derivative waveforms. We assess the performance of machine learning algorithms, including Decision Tree, k-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Among these algorithms, SVM outperformed the others, achieving an accuracy, precision, recall, and F1 score of 99%. This high accuracy underscores the potential of SVM in predicting vascular age. Accurate vascular age prediction within the healthy population has the potential to establish age-specific benchmarks, aiding in the detection of outliers indicative of accelerated cardiovascular aging or underlying cardiovascular diseases.