An AI-Driven IoT Wearable Framework for Real-Time Health Monitoring and Early Risk Prediction in Lymphoma Patients Using Hybrid CNN-LSTM
International Journal of Medical Toxicology and Forensic Medicine,
Vol. 16 (2026),
1 Dey 2026
,
Page 1-7
https://doi.org/10.22037/ijmtfm.v16.53262
Abstract
Background: Lymphoma is a complex form of hematological cancer, and its early symptoms of disease progression need continuous monitoring for prompt treatment. The report proposes using the Internet of Things (IoT) for real-time patient monitoring, featuring wearable sensors, Deep Learning (DL) models, and an intelligent alert system to enhance predictive assessment of patient health.
Methods: These smart wearable devices continuously measure physiological parameters, including heart rate, oxygen saturation (SpO₂), temperature, and electrocardiogram (ECG) parameters. A hybrid CNN-LSTM model is employed for physiological time-series analysis, while a ResNet-50 model enhanced with a Spatial Attention Mechanism (SAM) is used for histopathological classification of CLL, FL, and MCL. The reported performance of these models is evaluated separately according to their respective tasks. In addition, a weighted physiological risk score is used to identify abnormal physiological states from continuously monitored HR, SpO₂, temperature, and ECG measurements. An alert is generated when the risk score exceeds the predefined threshold. The framework is intended as an AI-assisted monitoring and decision-support system rather than an autonomous clinical diagnostic system.
Results: The experimental results demonstrated that the proposed CNN-LSTM model achieved an accuracy of 96.1%, a precision of 94.7%, a recall of 95.5%, an F1-score of 95.1%, and an inference time of 7ms for the physiological time-series analysis. Additionally, ResNet-50+SAM had a remarkably low inference time of 6 ms and stable five-fold cross-validation results (mean accuracy: 97.50 ± 0.14%).
Conclusion: This proposed framework offers numerous benefits to patients by enabling early detection of important health events and facilitating communication with healthcare professionals. This research demonstrates that artificial intelligence and IoT can transform real-time health monitoring, providing physicians with valuable information for personalized, ongoing care management of lymphoma patients.
- Lymphoma Monitoring, IoT-based Healthcare, CNN-LSTM, ResNet-50 with SAM, Risk Assessment, AI-driven Alerts, Wearable Sensors
How to Cite
References
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