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Article

The Role of BIA Analysis in Osteoporosis Risk Development: Hierarchical Clustering Approach

by
Giacinto Angelo Sgarro
1,†,
Luca Grilli
1,*,†,
Anna Antonia Valenzano
2,
Fiorenzo Moscatelli
2,
Domenico Monacis
3,
Giusi Toto
3,
Antonella De Maria
4,
Giovanni Messina
2,*,† and
Rita Polito
2
1
Department of Economics, Management and Territory (DEMeT) and Grant Office, University of Foggia, 71121 Foggia, Italy
2
Department of Clinical and Experimental Medicine, University of Foggia, 71122 Foggia, Italy
3
Department of Humanities, Letters, Cultural Heritage, Educational Sciences, University of Foggia, 71100 Foggia, Italy
4
Section of Human Physiology and Unit of Dietetics and Sports Medicine, Department of Experimental Medicine, University of Campania “Luigi Vanvitelli”, 80131 Naples, Italy
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diagnostics 2023, 13(13), 2292; https://doi.org/10.3390/diagnostics13132292
Submission received: 23 May 2023 / Revised: 21 June 2023 / Accepted: 3 July 2023 / Published: 6 July 2023
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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Abstract

Osteoporosis is a common musculoskeletal disorder among the elderly and a chronic condition which, like many other chronic conditions, requires long-term clinical management. It is caused by many factors, including lifestyle and obesity. Bioelectrical impedance analysis (BIA) is a method to estimate body composition based on a weak electric current flow through the body. The measured voltage is used to calculate body bioelectrical impedance, divided into resistance and reactance, which can be used to estimate body parameters such as total body water (TBW), fat-free mass (FFM), fat mass (FM), and muscle mass (MM). This study aims to find the tendency of osteoporosis in obese subjects, presenting a method based on hierarchical clustering, which, using BIA parameters, can group patients who show homogeneous characteristics. Grouping similar patients into clusters can be helpful in the field of medicine to identify disorders, pathologies, or more generally, characteristics of significant importance. Another added value of the clustering process is the possibility to define cluster prototypes, i.e., imaginary patients who represent models of “states”, which can be used together with clustering results to identify subjects with similar characteristics in a classification context. The results show that hierarchical clustering is a method that can be used to provide the detection of states and, consequently, supply a more personalized medicine approach. In addition, this method allowed us to elect BIA as a potential prognostic and diagnostic instrument in osteoporosis risk development.
Keywords: clustering; classification; body composition (BC); bioelectrical impedance analysis (BIA); osteoporosis clustering; classification; body composition (BC); bioelectrical impedance analysis (BIA); osteoporosis

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MDPI and ACS Style

Sgarro, G.A.; Grilli, L.; Valenzano, A.A.; Moscatelli, F.; Monacis, D.; Toto, G.; De Maria, A.; Messina, G.; Polito, R. The Role of BIA Analysis in Osteoporosis Risk Development: Hierarchical Clustering Approach. Diagnostics 2023, 13, 2292. https://doi.org/10.3390/diagnostics13132292

AMA Style

Sgarro GA, Grilli L, Valenzano AA, Moscatelli F, Monacis D, Toto G, De Maria A, Messina G, Polito R. The Role of BIA Analysis in Osteoporosis Risk Development: Hierarchical Clustering Approach. Diagnostics. 2023; 13(13):2292. https://doi.org/10.3390/diagnostics13132292

Chicago/Turabian Style

Sgarro, Giacinto Angelo, Luca Grilli, Anna Antonia Valenzano, Fiorenzo Moscatelli, Domenico Monacis, Giusi Toto, Antonella De Maria, Giovanni Messina, and Rita Polito. 2023. "The Role of BIA Analysis in Osteoporosis Risk Development: Hierarchical Clustering Approach" Diagnostics 13, no. 13: 2292. https://doi.org/10.3390/diagnostics13132292

APA Style

Sgarro, G. A., Grilli, L., Valenzano, A. A., Moscatelli, F., Monacis, D., Toto, G., De Maria, A., Messina, G., & Polito, R. (2023). The Role of BIA Analysis in Osteoporosis Risk Development: Hierarchical Clustering Approach. Diagnostics, 13(13), 2292. https://doi.org/10.3390/diagnostics13132292

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