Research on Disease Prediction Based on Daily Data Analysis of the Elderly Based on Neural Network Algorithms

In recent years, with the gradual arrival of an aging society, the health status of the elderly has attracted widespread attention. Against this background, this study aims to explore the potential value of these data for disease prediction by collecting and analyzing daily data of the elderly based on neural network algorithms. First, by using technical means such as smart sensing devices and mobile applications, we collected multi-dimensional data on the elderly's daily life, including but not limited to physiological indicators, activity levels, social interactions, and sleep patterns. These data are considered potential disease predictors because they provide comprehensive information about the physical condition and lifestyle of older adults. Second, we apply the collected data to a neural network algorithm, which has powerful learning and pattern recognition capabilities. By training models, neural networks can learn potential patterns and regularities from large amounts of complex data, thereby providing strong support for disease prediction. We pay special attention to the superiority of neural networks in processing multi-source heterogeneous data to more comprehensively understand the multi-faceted information of the elderly's health status. Through experiments and verification, we evaluate the performance of neural network algorithms in disease prediction in the elderly. This study explores disease prediction based on the daily data of the elderly based on neural network algorithms, aiming to provide scientific basis for establishing a more intelligent and accurate elderly health management system to promote the health and well-being of the elderly.