Analysis of the Effect of Health Literacy on Alzheimer's Disease Cognition Based on Machine Learning Regression Algorithm

Objective To understand the current situation of community residents' knowledge of Alzheimer's disease, analyze the impact of residents' health literacy on the awareness rate based on machine regression, and provide countermeasures and suggestions for promoting the public's corre...

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Veröffentlicht in:International Conference on Data Science and Business Analytics (Online) S. 560 - 565
Hauptverfasser: Li, Xue-Ying, Yang, Li-Jun, Xu, Li-Li, Liu, Jian-Ming
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2022
Schlagworte:
alzheimer's disease
Analytical models
cognition
Data models
Education
Health literacy
Machine learning
Machine learning algorithms
Prediction algorithms
Predictive models
regression algorithm
ISSN:2831-5944
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Zusammenfassung:Objective To understand the current situation of community residents' knowledge of Alzheimer's disease, analyze the impact of residents' health literacy on the awareness rate based on machine regression, and provide countermeasures and suggestions for promoting the public's correct understanding and reasonable access to Alzheimer's disease related information. Methods In 2022, the residents of a community in Weifang City were sampled by stratified random sampling, and a prediction model of Alzheimer's disease cognitive level based on machine learning regression algorithm was proposed, and a common typical regression algorithm was used to predict the cognitive status based on health literacy level. Results The basic knowledge awareness rate was 34.7%, and the importance of the influencing factors was ranked according to the results of machine regression: health literacy, gender, education level, marital status, and average annual family income. In the dimension of skill awareness rate, the skill knowledge awareness rate was 14.0%, and the importance of the influencing factors was ranked according to the results of the machine learning regression algorithm, which was the average annual household income, occupation, education level, and type of residence. In the dimension of disease stress, disease stress accounted for 35.7%, and the top three influencing factors in the importance of machine regression results were health literacy, occupation, and chronic disease. Conclusion Through the comparison of the evaluation indicators of the prediction model, the prediction of the accurate cognitive level of Alzheimer's disease is effectively predicted, which provides a scientific basis for improving the cognitive level and health outcomes of Alzheimer's disease.
ISSN:2831-5944
DOI:10.1109/ICDSBA57203.2022.00043