Damera Vijay Kumar, Cheripelli Ramesh, Putta Narsaiah, Sirisha G, Kalavala Deepthi
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad Campus, Hyderabad, India.
Department of Information Technology, Vidya Jyothi Institute of Technology, Hyderabad, India.
Sci Rep. 2025 Jul 5;15(1):24088. doi: 10.1038/s41598-025-09727-z.
The rapid advancement of the Internet of Medical Things (IoMT) has revolutionized remote healthcare monitoring, enabling real-time disease detection and patient care. This research introduces a novel AI-driven telemedicine framework that integrates IoMT, cloud computing, and wireless sensor networks for efficient healthcare monitoring. A key innovation of this study is the Transformer-based Self-Attention Model (TL-SAM), which enhances disease classification by replacing conventional convolutional layers with transformer layers. The proposed TL-SAM framework effectively extracts spatial and spectral features from patient health data, optimizing classification accuracy. Furthermore, the model employs an Improved Wild Horse Optimization with Levy Flight Algorithm (IWHOLFA) for hyperparameter tuning, enhancing its predictive performance. Real-time biosensor data is collected and transmitted to an IoMT cloud repository, where AI-driven analytics facilitate early disease diagnosis. Extensive experimentation on the UCI dataset demonstrates the superior accuracy of TL-SAM compared to conventional deep learning models, achieving an accuracy of 98.62%, precision of 97%, recall of 98%, and F1-score of 97%. The study highlights the effectiveness of AI-enhanced IoMT systems in reducing healthcare costs, improving early disease detection, and ensuring timely medical interventions. The proposed approach represents a significant advancement in smart healthcare, offering a scalable and efficient solution for remote patient monitoring and diagnosis.
医疗物联网（IoMT）的迅速发展彻底改变了远程医疗监测，实现了实时疾病检测和患者护理。本研究引入了一种新颖的人工智能驱动的远程医疗框架，该框架集成了IoMT、云计算和无线传感器网络，以实现高效的医疗监测。本研究的一项关键创新是基于Transformer的自注意力模型（TL-SAM），它通过用Transformer层替换传统的卷积层来增强疾病分类。所提出的TL-SAM框架有效地从患者健康数据中提取空间和光谱特征，优化分类准确率。此外，该模型采用了带有莱维飞行算法的改进野马优化算法（IWHOLFA）进行超参数调整，提高其预测性能。实时生物传感器数据被收集并传输到IoMT云存储库，在那里人工智能驱动的分析有助于早期疾病诊断。在UCI数据集上进行的大量实验表明，与传统深度学习模型相比，TL-SAM具有更高的准确率，达到了98.62%的准确率、97%的精确率、98%的召回率和97%的F1分数。该研究强调了人工智能增强的IoMT系统在降低医疗成本、改善早期疾病检测和确保及时医疗干预方面的有效性。所提出的方法代表了智能医疗的重大进步，为远程患者监测和诊断提供了一种可扩展且高效的解决方案。
