Current Volume 9
Personalized Multi-Modal Wearable Data-Based Heart Risk Prediction System Using Machine Learning
Abstract
Heart and circulatory conditions remain a leading cause of death globally, and they often worsen quietly before obvious symptoms appear. Consumer wearables now make it practical to collect physiology around the clock, yet many pipelines still lean on one signal at a time and collapse decisions into coarse “normal vs abnormal” labels that arrive late for prevention. This work outlines a personalized, multi-modal wearable pipeline that jointly uses ECG, photoplethysmography (PPG), heart-rate variability (HRV), blood-oxygen saturation (SpO₂), and sleep-related cues. Machine learning sits on top of adaptive baselines so the model can learn what is typical for a given person, not only population averages, and it emits a continuous risk score from 0 to 100 that is easier to track over days and weeks. Explainable AI (XAI) is folded in so users and clinicians can see which factors moved the score, supporting trust and safer follow-up. Overall, the aim is to shift monitoring from reactive firefighting toward earlier, individualized cardiovascular vigilance.
Keywords
Cardiovascular Disease, Wearable Health Monitoring, Machine Learning, Multi-Modal Data Fusion, Heart Risk Prediction, Explainable Artificial Intelligence
Citations
IRE Journals:
Mohammed Fahad, Dr. Haripriya V. "Personalized Multi-Modal Wearable Data-Based Heart Risk Prediction System Using Machine Learning" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2617-2621 https://doi.org/10.64388/IREV9I11-1717965
IEEE:
Mohammed Fahad, Dr. Haripriya V.
"Personalized Multi-Modal Wearable Data-Based Heart Risk Prediction System Using Machine Learning" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717965
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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