# Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep Learning

arXiv: https://arxiv.org/abs/2305.05648 · HTML: https://mlsanity.com/paper/2305.05648/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 69 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 3 of 3)

- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Seeing Red: PPG Biometrics Using Smartphone Cameras](https://mlsanity.com/paper/2004.07088/) (2020)

## Built on by (most cited first, 5 of 5)

- [PaPaGei: Open Foundation Models for Optical Physiological Signals](https://mlsanity.com/paper/2410.20542/) (2024)
- [CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Models](https://mlsanity.com/paper/2512.02180/) (2025)
- [Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals](https://mlsanity.com/paper/2309.05843/) (2023)
- [Hybrid Modeling of Photoplethysmography for Non-invasive Monitoring of Cardiovascular Parameters](https://mlsanity.com/paper/2511.14452/) (2025)
- [An AI-Enabled Framework Within Reach for Enhancing Healthcare Sustainability and Fairness](https://mlsanity.com/paper/2406.07558/) (2024)

## Abstract (arXiv)

Cardiovascular diseases (CVDs) are responsible for a large proportion of premature deaths in low- and middle-income countries. Early CVD detection and intervention is critical in these populations, yet many existing CVD risk scores require a physical examination or lab measurements, which can be challenging in such health systems due to limited accessibility. Here we investigated the potential to use photoplethysmography (PPG), a sensing technology available on most smartphones that can potentially enable large-scale screening at low cost, for CVD risk prediction. We developed a deep learning PPG-based CVD risk score (DLS) to predict the probability of having major adverse cardiovascular events (MACE: non-fatal myocardial infarction, stroke, and cardiovascular death) within ten years, given only age, sex, smoking status and PPG as predictors. We compared the DLS with the office-based refit-WHO score, which adopts the shared predictors from WHO and Globorisk scores (age, sex, smoking status, height, weight and systolic blood pressure) but refitted on the UK Biobank (UKB) cohort. In UKB cohort, DLS's C-statistic (71.1%, 95% CI 69.9-72.4) was non-inferior to office-based refit-WHO score (70.9%, 95% CI 69.7-72.2; non-inferiority margin of 2.5%, p<0.01). The calibration of the DLS was satisfactory, with a 1.8% mean absolute calibration error. Adding DLS features to the office-based score increased the C-statistic by 1.0% (95% CI 0.6-1.4). DLS predicts ten-year MACE risk comparable with the office-based refit-WHO score. It provides a proof-of-concept and suggests the potential of a PPG-based approach strategies for community-based primary prevention in resource-limited regions.