Design of health information management model for elderly care using an advanced higher-order hybrid clustering algorithm from the perspective of sports and medicine integration.

In the context of integrating sports and medicine domains, the urgent resolution of elderly health supervision requires effective data clustering algorithms. This paper introduces a novel higher-order hybrid clustering algorithm that combines density values and the particle swarm optimization (PSO) algorithm. Initially, the traditional PSO algorithm is enhanced by integrating the Global Evolution Dynamic Model (GEDM) into the Distribution Estimation Algorithm (EDA), constructing a weighted covariance matrix-based GEDM. This adapted PSO algorithm dynamically selects between the Global Evolution Dynamic Model and the standard PSO algorithm to update population information, significantly enhancing convergence speed while mitigating the risk of local optima entrapment. Subsequently, the higher-order hybrid clustering algorithm is formulated based on the density value and the refined PSO algorithm. The PSO clustering algorithm is adopted in the initial clustering phase, culminating in class clusters after a finite number of iterations. These clusters then undergo the application of the density peak search algorithm to identify candidate centroids. The final centroids are determined through a fusion of the initial class clusters and the identified candidate centroids. Results showcase remarkable improvements: achieving 99.13%, 82.22%, and 99.22% for F-measure, recall, and precision on dataset S1, and 75.22%, 64.0%, and 64.4% on dataset CMC. Notably, the proposed algorithm yields a 75.22%, 64.4%, and 64.6% rate on dataset S, significantly surpassing the comparative schemes' performance. Moreover, employing the text vector representation of the LDA topic vector model underscores the efficacy of the higher-order hybrid clustering algorithm in efficiently clustering text information. This innovative approach facilitates swift and accurate clustering of elderly health data from the perspective of sports and medicine integration. It enables the identification of patterns and regularities within the data, facilitating the formulation of personalized health management strategies and addressing latent health concerns among the elderly population.

The Relationship between Dietary Patterns and Bone Mineral Density of 476 Middle-Aged and Aged People.

BACKGROUND: To investigate the relationship between different dietary patterns and the levels of bone mineral density (BMD) in middle-aged and aged people, and to provide references for the nutritional prevention of osteoporosis. METHODS: A total of 476 residents aged 45 yr or more in Qiqihar City were enrolled from Aug 2018 to Feb 2019. They took a Food Frequency Questionnaire for dietary survey. Their dietary patterns were analyzed using the factor analysis method, and BMD were detected using ultrasound bone densitometer, to explore the relationship between different dietary patterns and BMD levels. RESULTS: Four dietary patterns were obtained in the survey: relatively balanced, oil-salt, milk-tuber, and aquatic. Among them, the prevalence of osteoporosis reached 21.8%. High-level relatively balanced dietary pattern (OR=0.588, 95%CI= 0.363-0.951) and high-level dairy-potato food dietary pattern (OR=0.668, 95%CI= 0.370-0.983) were associated with lower risk of osteoporosis. CONCLUSION: A balanced diet and a high intake of dairy-potato food dietary pattern were associated with a lower prevalence of osteoporosis. It is recommended that middle-aged and aged people should have a balanced diet with more dairy products and potatoes to protect bone health.