Open Access

ARTICLE

Statistical Medical Pattern Recognition for Body Composition Data Using Bioelectrical Impedance Analyzer

Florin Valentin Leuciuc^1,2,*, Maria Daniela Craciun^1,2, Iulian Stefan Holubiac¹, Mazin Abed Mohammed³, Karrar Hameed Abdulkareem⁴, Gheorghe Pricop^1,2

1 Stefan cel Mare University of Suceava, Suceava, 720229, Romania
2 The Interdisciplinary Research Center for Human Motricity and Health Sciences, Suceava, 720229, Romania
3 College of Computer Science and Information Technology, University of Anbar, Ramadi, 31001, Iraq
4 College of Agriculture, Al-Muthanna University, Samawah, 66001, Iraq

* Corresponding Author: Florin Valentin Leuciuc. Email:

(This article belongs to this Special Issue: Retrospective Big Data Analytics in Radiological Imaging for Precision Medicine)

Computers, Materials & Continua 2021, 67(2), 2601-2617. https://doi.org/10.32604/cmc.2021.014863

Received 22 October 2020; Accepted 07 December 2020; Issue published 05 February 2021

Abstract

Identifying patterns, recognition systems, prediction methods, and detection methods is a major challenge in solving different medical issues. Few categories of devices for personal and professional assessment of body composition are available. Bioelectrical impedance analyzer is a simple, safe, affordable, mobile, non-invasive, and less expensive alternative device for body composition assessment. Identifying the body composition pattern of different groups with varying age and gender is a major challenge in defining an optimal level because of the body shape, body mass, energy requirements, physical fitness, health status, and metabolic profile. Thus, this research aims to identify the statistical medical pattern recognition of body composition data by using a bioelectrical impedance analyzer. In previous studies, a pattern was identified for four indicators that concern body composition (e.g., body mass index (BMI), body fat, muscle mass, and total body water). The novelty of our study is the fact that we identified a recognition pattern by using medical statistical methods for a body composition that contains seven indicators (e.g., body fat, visceral fat, BMI, muscle mass, skeletal muscle mass, sarcopenic index, and total body water). The youth that exhibited the body composition pattern identified in our study could be considered healthy. Every deviation of one or more parameters outside the margins of the pattern for body composition could be associated with health issues, and more medical investigations would be needed for a diagnosis. BIA is considered a valid and reliable device to assess body composition along with medical statistical methods to identify a pattern for body composition according to the age, gender, and other relevant parameters.

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Keywords

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