IoT-Assisted Cloud Computing Architecture for Alzheimer's Disease Detection Using LSTM Networks

Abstract

The early diagnosis of the disease remains one of the significant challenges in healthcare, making available advanced, scalable, and efficient diagnostic solutions for the condition imperative. Conventional diagnostics are usually resource-intensive, time-consuming, and fail to be performed in real-time. In this paper we propose an IoT-assisted cloud computing architecture with the use of LSTM networks to detect Alzheimer disease accurately and in a timely manner. This integrated solution exhibits IoT-enabled wearable devices, providing us with real-time data, cloud computing that provides a robust core of processing such data, and finally, we have LSTM networks that help us analyze time-series data of our patients, whose psychometric tests, cognitive performances, and physiological signals, etc. In contrast to past approaches that depend on static data sets and centralized processing, our framework provides scalability, real-time processing, and incremental learning through distributed cloud architecture. Experimental evaluations on clinical datasets show a detection accuracy of 92% which outperforms state-of-the-art models by 15%. Moreover, the thin IoT framework of system guarantees the efficient data transfer, bringing down the latency by 30%, than the traditional architectures. This game changing technology gives medical professionals an early diagnostic tool to intervene earlier in Alzheimer’s disease, so that more personalized treatment is possible. IoT-cloud synergy in health care presents a significant paradigm shift toward sustainable health management by not just eliminating the barriers between early disease detection and patient treatment but also by offering a scalable framework to empower patients to take proactive measures that improve their health status.