Keywords
Body Composition
Adipose Tissue
Body Mass Index
Obesity
Overweight
Data Mining
How to Cite
Abstract
Introduction: The Body Mass Index (BMI) is insufficient to diagnose obesity. This document presents the Fat Mass Index (FMI) as the most accurate indicator for the diagnosis of this disease given its high level of connection with respect to the variables of fatty tissue.
Objective: To identify the most relevant kinanthropometric variables to determine the FMI as the most appropriate tool for the diagnosis of obesity in the population that attended an outpatient consultation.
Methodology: the retrospective study was developed with a population of 899 between 6 and 81 years of age. Data were obtained by bioimpedance measurement (Inbody® 770) over a two-year period (2017-2019). The information was refined using the CRISP-DM data mining methodology and statistically analyzed using the SPSS statistical program. Finally, the anthropometric variables associated with fatty tissue are related to the FMI and BMI by means of the coefficient of determination (R2).
Results: It will be reduced that the variables with the greatest relationship with fat mass are: hip circumference measurement, neck circumference measurement, abdomen circumference measurement and weight. However, the importance of this relationship depends on gender.
Conclusion: The relevance of this study is the calculation of fat mass from anthropometric measurements, in order to obtain the IMG and since the latter is specific for the diagnosis of overweight/obesity, the implementation of kinanthropometry in the consultation of patients with obesity. It is crucial for an adequate approach to the patient, as it does not have the technology to determine body composition.
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