| Title: | Age estimation and cardiac disease prediction from PPG signals using interpretable machine learning models. |
| Lead Institution: | Duke University |
| Principal investigator: |
Application 549696
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About
Our study aims to develop a new, more accurate way of predicting heart health by using interpretable machine learning to assess biological aging. Unlike chronological age, which only reflects the years a person has lived, biological age can provide insights into the actual health of their cardiovascular system. For this, we will analyze Photoplethysmography (PPG) signals-light-based measurements often used to monitor blood flow-to estimate how a person's heart and blood vessels are aging. The goals of this research are threefold: (1) Create an interpretable machine learning model that can predict a patient's cardiovascular age accurately, (2) Identify and explain the specific biological factors influencing these age predictions, and (3) Investigate the relationship between chronological age and biological age to better understand how they align or differ in the context of heart health. The scientific rationale behind this study is to bridge the gap between chronological and biological age measurements. Many people with the same chronological age can have vastly different cardiovascular health, which could mean that some individuals are at risk of heart disease sooner than expected. By focusing on biological age, we hope to provide a more personalized assessment of cardiovascular health, leading to better preventative care. We plan to disseminate out finding through manuscript publication, and the release of our model and code in a publicly accessible repository.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 17959 | Data-hugging shields proprietary AI models from research that could disprove them | Anish Karpurapu (+3) | 2026 | npj Artificial Intelligence |
Enabling scientific discoveries that improve human health