โก Quick Summary
The study introduces Anston, an innovative model designed to predict disease risks in the elderly using physiological indicators and pathogenic factors. With an impressive accuracy of 95% and other significant metrics, Anston aims to enhance elderly care in nursing homes.
๐ Key Details
- ๐ Dataset: Public datasets and subject data
- ๐งฉ Features used: Physiological indicators and pathogenic factors
- โ๏ธ Technology: Anston (Attention Mechanism Network Model)
- ๐ Performance: Accuracy 95%, Precision 92%, Recall 91%, Specificity 93%, F1 Score 91%, AUC 93%
๐ Key Takeaways
- ๐ง Anston utilizes an attention mechanism for structured data classification.
- ๐ก Data enhancement methods address sample shortages and imbalances.
- ๐ Feature weight updates are automated to improve prediction accuracy.
- ๐ฅ Targeted at nursing homes for proactive elderly care.
- ๐ Achieved state-of-the-art (SOTA) results in disease risk prediction.
- ๐ Potential for broader applications in smart aging and healthcare.
- ๐ฉโ๐ฌ Study conducted by Zhou F, Hu S, Du X, and Lu Z.
- ๐ Published in: Sci Rep, 2025.
๐ Background
As the global population ages, the demand for effective healthcare solutions for the elderly increases. Nursing homes face significant challenges in predicting and managing disease risks among residents. The development of predictive models like Anston is crucial for enhancing elderly care and alleviating pressure on public health services.
๐๏ธ Study
The study focused on creating a robust model for predicting disease risks in elderly individuals. By leveraging physiological indicators and pathogenic factors, the researchers designed Anston, which incorporates advanced techniques such as data enhancement and an attention mechanism network. This innovative approach aims to provide nursing homes with reliable tools for proactive health management.
๐ Results
The Anston model demonstrated remarkable performance, achieving an accuracy of 95%, precision of 92%, recall of 91%, specificity of 93%, F1 score of 91%, and an AUC of 93%. These results indicate that Anston is not only effective but also sets a new benchmark in the field of disease risk prediction for the elderly.
๐ Impact and Implications
The implications of this study are profound. By implementing the Anston model, nursing homes can significantly improve their ability to predict and manage disease risks, leading to better health outcomes for residents. This advancement in elderly care technology could pave the way for more personalized and efficient healthcare solutions, ultimately enhancing the quality of life for the aging population.
๐ฎ Conclusion
The introduction of the Anston model marks a significant step forward in the realm of elderly care. With its impressive predictive capabilities, it holds the potential to transform how nursing homes approach disease risk management. As we continue to explore the integration of technology in healthcare, models like Anston will be essential in addressing the challenges posed by an aging society.
๐ฌ Your comments
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Anston attentional network for structured data based stroke risk prediction in smart aging.
Abstract
To reduce the pressure on public health services caused by the aging population, nursing homes need to predict disease risks for the elderly periodically. To improve the disease risks predicting ability of nursing homes, we designed Anston (An Attention Mechanism Network Model for Structured Data Classification) in the application scenario of innovative elderly care. The Anston model can use the physiological indicators and pathogenic factors easily collected by nursing homes to predict disease risks. In the study of disease risk prediction based on physiological indicators and pathogenic factors for thoughtful elderly care, we designed a data enhancement method, a feature weight automatic update method, and a multi-layer perceptron neural network to solve the problems of sample shortage, inconsistent feature weights, and sample imbalance. At the same time, we designed an attention mechanism network model for structured data classification based on the multi-layer perceptron neural network Developed in this paper. To fit the application scenario of competent elderly care, we propose a disease risk prediction model, Anston, based on the data enhancement method, feature automatic update method, and structured data classification attention mechanism network designed in this paper. We use public data sets and subject data as sample data in the experiment. The experimental results show that the Anston model has an accuracy of 95%, a precision of 92%, a recall of 91%, a specificity of 93%, an F1 score of 91%, and an AUC of 93% in predicting disease risks in the experiment, which have achieved the SOTA result.
Author: [‘Zhou F’, ‘Hu S’, ‘Du X’, ‘Lu Z’]
Journal: Sci Rep
Citation: Zhou F, et al. Anston attentional network for structured data based stroke risk prediction in smart aging. Anston attentional network for structured data based stroke risk prediction in smart aging. 2025; 15:34926. doi: 10.1038/s41598-025-18758-5