Effective Public Health Assessment: Research on Alzheimer Disease Diagnosis Model With Communication Technology

Abstract

Rapid identification and appropriate management of Alzheimer's pose important challenges, particularly within public health contexts. This research introduces a unique framework for diagnosing Alzheimer's disease using communication technologies (such as wearable sensors). Novel chimp search-driven ReLu recurrent network (CS-RRN) strategy is introduced in this study for effectively detecting Alzheimer's disease. The ReLu function is employed in long/short-term memory (LSTM) to learn nonlinear dependencies, and then the chimp optimization strategy is applied to enhance the detection performance of the LSTM network. The gait signal data is collected from various wearable sensor devices to analyze the effectiveness of the suggested CS-RRN m technique. The performance of the proposed method is analyzed through a Python-driven implementation platform in terms of various metrics. The CS-RRN approach effectively achieved the maximum performance in detecting Alzheimer's disease when compared with other existing approaches. Based on the findings of this research, communication technology may substantially boost public health by providing a low-cost and high-performing method for Alzheimer disease identification.