Integrating Wearable Sensor Data with Machine Learning for Early Detection of Non-Communicable Diseases

• Author(s): Bamidele Samuel Adelusi ; Damilola Osamika ; MariaTheresa Chinyeaka Kelvin-Agwu ; Ashiata Yetunde Mustapha ; Nura Ikhalea
• Paper ID: 1705169
• Page: 647-659
• Published Date: 31-10-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 4 October-2023

Abstract

The early detection of non-communicable diseases (NCDs) is critical for improving patient outcomes and reducing healthcare costs. Integrating wearable sensor data with machine learning (ML) presents a transformative approach to detecting NCDs such as cardiovascular diseases, diabetes, and respiratory disorders in real-time. Wearable sensors continuously monitor vital signs, physical activity, and other physiological parameters, generating vast amounts of data that can be analyzed using advanced ML algorithms. This explores the potential of wearable sensor-based ML models in predicting NCD risk factors and detecting early disease onset. This discuss key sensor modalities, including heart rate monitors, continuous glucose monitors, and smartwatches, as well as data preprocessing techniques such as noise reduction, feature extraction, and normalization. Supervised and unsupervised learning methods, including deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are evaluated for their effectiveness in analyzing time-series health data. Despite significant advancements, challenges such as data privacy, standardization, and model interpretability hinder widespread adoption. Ensuring compliance with regulations like HIPAA and GDPR, improving interoperability between wearable devices, and addressing biases in ML models are crucial for reliable real-world deployment. Furthermore, federated learning and blockchain-based security frameworks offer promising solutions for privacy-preserving healthcare analytics. The integration of wearable sensors with ML has the potential to revolutionize preventive healthcare by enabling continuous, real-time monitoring and early intervention. Future research should focus on improving model robustness, enhancing sensor accuracy, and establishing clinical validation frameworks to bridge the gap between research and practical implementation. By leveraging AI-driven insights from wearable devices, healthcare systems can move toward personalized and proactive disease management, ultimately reducing the burden of NCDs worldwide.

Keywords

Wearable sensor, Machine learning, Early detection, Non-communicable diseases

Citations

IRE Journals:
Bamidele Samuel Adelusi , Damilola Osamika , MariaTheresa Chinyeaka Kelvin-Agwu , Ashiata Yetunde Mustapha , Nura Ikhalea "Integrating Wearable Sensor Data with Machine Learning for Early Detection of Non-Communicable Diseases" Iconic Research And Engineering Journals Volume 7 Issue 4 2023 Page 647-659

IEEE:
Bamidele Samuel Adelusi , Damilola Osamika , MariaTheresa Chinyeaka Kelvin-Agwu , Ashiata Yetunde Mustapha , Nura Ikhalea "Integrating Wearable Sensor Data with Machine Learning for Early Detection of Non-Communicable Diseases" Iconic Research And Engineering Journals, 7(4)