Department of Computer, Science and Engineering, Anna University, India.
Department of Computer Science and Engineering, Ponjesly College of Engineering, India.
Biomed Res Int. 2022 Feb 11;2022:5038851. doi: 10.1155/2022/5038851. eCollection 2022.
Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily accessible biomarkers for neurodegenerative disorders, namely, Alzheimer's disease (AD) are needed urgently to assist the diagnoses at its early stages. Due to the severe situations, these systems demand high-quality qualities including availability and accuracy. Deep learning algorithms are promising in such health applications when a large amount of data is available. These solutions are ideal for a distributed blockchain-based IoT system. A good Internet connection is critical to the speed of these system responses. Due to their limited processing capabilities, smart gateway devices cannot implement deep learning algorithms. In this paper, we investigate the use of blockchain-based deep neural networks for higher speed and delivery of healthcare data in a healthcare management system. The study exhibits a real-time health monitoring for classification and assesses the response time and accuracy. The deep learning model classifies the brain diseases as benign or malignant. The study takes into account three different classes to predict the brain disease as benign or malignant that includes AD, mild cognitive impairment, and normal cognitive level. The study involves a series of processing where most of the data are utilized for training these classifiers and ensemble model with a metaclassifier classifying the resultant class. The simulation is conducted to test the efficacy of the model over that of the OASIS-3 dataset, which is a longitudinal neuroimaging, cognitive, clinical, and biomarker dataset for normal aging and AD, and it is further trained and tested on the UDS dataset from ADNI. The results show that the proposed method accurately (98%) responds to the query with high speed retrieval of classified results with an increased training accuracy of 0.539 and testing accuracy of 0.559.
远程健康监测有助于在早期阶段预防疾病。物联网 (IoT) 概念最近取得了进展，实现了无处不在的监测。迫切需要用于神经退行性疾病（即阿尔茨海默病（AD））的易于获取的生物标志物，以协助在早期阶段进行诊断。由于情况严重，这些系统要求具有可用性和准确性等高质量。当有大量数据可用时，深度学习算法在这些健康应用中很有前途。这些解决方案非常适合基于区块链的分布式物联网系统。良好的互联网连接对于这些系统响应的速度至关重要。由于其处理能力有限，智能网关设备无法实现深度学习算法。在本文中，我们研究了在医疗保健管理系统中使用基于区块链的深度神经网络来提高速度和传输医疗保健数据。该研究展示了实时健康监测的分类，并评估了响应时间和准确性。深度学习模型将脑部疾病分类为良性或恶性。该研究考虑了三种不同的类别，以预测脑部疾病为良性或恶性，包括 AD、轻度认知障碍和正常认知水平。该研究涉及一系列处理，其中大部分数据用于训练这些分类器和集成模型，并使用分类器对结果类进行分类。该模拟用于测试模型的功效，该模型超过了 OASIS-3 数据集，该数据集是一个纵向神经影像学、认知、临床和生物标志物数据集，用于正常衰老和 AD，并且进一步在 ADNI 的 UDS 数据集上进行训练和测试。结果表明，该方法可以准确（98%）响应查询，具有高速检索分类结果的特点，训练准确性提高了 0.539，测试准确性提高了 0.559。
