Home / Current Issue / Paper 1717965
Personalized Multi-Modal Wearable Data-Based Heart Risk Prediction System Using Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Healthcare & Public Health
DOI: https://doi.org/10.64388/IREV9I11-1717965
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
References
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[3] A. Sony et al., "Predictive Health Monitoring System for Patient Risk Assessment," IEEE (CONIT), 2025.
[4] G. Gupta et al., "Deep Learning-Based Forecasting of Spontaneous Termination of Cardiac Episodes," Nature Scientific Reports, 2023.
[5] S. Guruprasad et al., "Heart Disease Prediction Using Machine Learning Techniques," ICEECCOT, 2021.
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How to cite this paper
@article{1717965,
author = {Mohammed Fahad, Dr. Haripriya V.},
title = {Personalized Multi-Modal Wearable Data-Based Heart Risk Prediction System Using Machine Learning},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {2617-2621},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717965.pdf},
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},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717965}
}