Data analysis tool for identifying multidimensional health profiles associated with frailty in older adults

Frailty in older adults is a multidimensional condition associated with physical, psychological, and sociodemographic factors that may affect functionality and quality of life during aging. The analysis of these factors often requires the management of heterogeneous information, which can represent a challenge for specialists during evaluation and follow-up processes. In this context, computational tools and data-driven approaches may support the organization and exploratory analysis of multidimensional health information. This study presents the development of a web-based computational system designed to support specialists in the collection, management, and analysis of health-related data from older adults in the State of Hidalgo, México.

A web platform was developed to register and organize sociodemographic, psychological, and physical information from older adults. The system integrated questionnaires, physical measurements, statistical analysis tools, and computational intelligence techniques within a single environment. Data from 210 participants were collected and analyzed using exploratory statistical methods, feature selection techniques, and K-means clustering to identify patterns associated with functional and health-related characteristics. Internal validation metrics, including the Silhouette coefficient, Davies-Bouldin Index, and Calinski-Harabasz Index, were used to support cluster evaluation.

The proposed system facilitated the structured storage, visualization, and exploratory analysis of multidimensional health information. The clustering analysis identified two differentiated groups characterized by distinct physical and psychological profiles. Differences were observed in variables such as muscular strength, calf circumference, body mass index, gratitude, and functional self-perception. The interpretation of the obtained profiles considered the demographic composition of the groups, particularly the predominance of women in one cluster, which may influence physical measurements associated with aging. Additionally, the usability assessment conducted with specialists using the SUS instrument indicated an acceptable level of usability for exploratory and research support purposes.

The results suggest that integrating computational intelligence and statistical analysis techniques into a unified platform may support the exploratory assessment of multidimensional factors related to aging and frailty in older adults. The proposed system does not aim to establish clinical diagnoses, but rather to provide specialists with an additional resource for organizing and interpreting complex health-related information. This approach may contribute to the development of data-driven tools to support gerontological research and comprehensive evaluation processes in older populations.

Not applicable.