Health Literacy Prediction of the Elderly Based on SMOTE Algorithm and Machine Learning

In view of the limitations of the traditional whale algorithm in the research on health literacy prediction of the elderly, a health literacy prediction research scheme based on SMOTE algorithm and machine learning was proposed. Firstly, the influencing factors is accurately located through the data synthesis sampling theory, and the indicators is reasonably divided to reduce interference, and the SMOTE algorithm and machine is used to construct a health literacy prediction research scheme. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional whale algorithm in terms of health literacy prediction research accuracy and processing time of influencing factors, which has obvious advantages. Health literacy prediction research plays an extremely important role in the health of the elderly, which can accurately predict and optimize the growth characteristics and product generation of the elderly. However, the traditional whale algorithm has certain limitations in solving the problem of literacy prediction and simulation, especially when dealing with complex problems. In this paper, this paper proposes a health literacy prediction research scheme based on SMOTE algorithm and machine to better solve this problem. In this scheme, the influencing factors is accurately located through the data synthesis sampling theory, so as to determine the division of indicators, and the SMOTE algorithm and machine is used to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on SMOTE algorithm and machine can better solve the limitations of the traditional whale algorithm and improve the simulation accuracy and efficiency in the prediction of health literacy of the elderly.