ItemConference Paper

Redefining Disease Detection: Innovative Machine Learning and Wearable Sensor Integration

Title
Redefining Disease Detection: Innovative Machine Learning and Wearable Sensor Integration
Creator
Mehta, Pankaj; Desai, Amruta; Dutta, Shuvo; Sanjaya, P.R.; Asha, M.S.; Aravindan, M.
Description
Wearable sensor technology is considered to be one of the fastest growing fields of information and communication technologies and it has revolutionized the healthcare delivery by enabling continuous and real-time physiological monitoring. This research presents a novel approach that allows an early onset disease detection instigated with the prowess of advanced Graph Neural Network (GNNs) matched with the body streams gathered from wearable machines using its implementation technology - Pythonline of programming named Awesome Geometric libraries referred to as Aztec PyTorch. Graph neural networks (GNNs) are especially suitable within the scope of modeling complex relationships among multivariate inputs of the sensors for modeling the temporal and spatial subjacent dependence of the physiological signs with regards to reality. The proposed system analyzes the data acquired from the various wearable sensors such as heart rate, accelerometers and bio sensors, which help in anomaly detection and hence the detection of the patient having cardiovascular, metabolic and neurological diseases. The synergy between innovative deep learning models and sensors as ubiquitous technologies offers great promise to transform the provision of personalised healthcare services and dealing with disease in its early stages. 2025 IEEE.
Source
Proceedings of 2025 10th International Conference on Science Technology, Engineering and Mathematics, ICONSTEM 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Disease Detection; Graph Neural Networks (GNNs); Personalized Healthcare; Predictive Analytics; PyTorch Geometric; Wearable Sensors
Coverage
Mehta P., University Institute of Biotechnology, Department of Biosciences, Chandigarh University, Panjab, India; Desai A., School of Sciences, Pimpri Chinchwad University, Pune, India; Dutta S., Western Michigan University, Department of Physics, Kalamazoo, MI, United States; Sanjaya P.R., College of Dentistry, University of Hail, Department of Basic Dental & Medical Sciences, Hail province, Saudi Arabia; Asha M.S., Christ(Deemed to Be University), Bengalure, India; Aravindan M., Agni College of Technology, Department of Electrical and Electronics Engineering, Chennai, Thazhambur, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833156187-1;
Format
online
Language
English
Type
Conference paper

Citation

Mehta, Pankaj; Desai, Amruta; Dutta, Shuvo; Sanjaya, P.R.; Asha, M.S.; Aravindan, M., “Redefining Disease Detection: Innovative Machine Learning and Wearable Sensor Integration,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26078.

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