Automatic segmentation to characterize anthropometric parameters and cardiovascular indicators in children

Autor(es):Mostrar menos -

Goroso, Daniel Gustavo ^[1] ; Fraga, Alvaro ^[2] ; Macedo, Michel ^[1] ; Fernanda de Miranda Rodrigues, Carla ^[1] ; Mendes de Oliveira Silva, Bruno ^[1] ; Watanabe, William Tsutomu ^[1] ; Pereira da Silva, Diego ^[1] ; Rodrigues da Silva, Robson ^[1] ; Puglisi, Jose Luis ^[3] ; Marcin, James ^[4] ; Dharmar, Madan ^[4]

Afiliação do(s) autor(es)

^[1] Univ Mogi das Cruzes, Res & Technol Ctr, Mogi Das Cruzes, SP - Brazil

^[2] Consejo Nacl Invest Cient & Tecn, Inst Desarrollo & Diseno INGAR, Santa Fe - Argentina

^[3] Calif North State Univ, Coll Med, Sacramento, CA - USA

^[4] Univ Calif, Ctr Hlth & Technol, Sacramento, CA - USA

• Tipo de documento: Artigo Científico
• Fonte: WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY; v. 11, n. 4 JUL 2021.
• Citações Web of Science: 0

Resumo

A new predictive model to classify childhood obesity was implemented using machine learning techniques. The first step was to calculate the most relevant anthropomorphic and cardiovascular parameters of 187 children through principal component analysis (PCA) and cluster classification. Then Naive-Bayes method classified these children into six groups using anthropometric Z Score, measurements of abdominal obesity, and arterial pressure: Group I (20.32% of total): composed mainly by accentuated malnutrition and malnutrition children; Group II (36.36%): composed primarily by eutrophic children; Group III (21.4%): constituted by eutrophic plus overweight children; Group IV (14.97%): comprised mainly by overweight and obese children; Group V (5.34%): Obese and overweight children; and Group VI (1.6%): obese at risk children. From Group II to VI, the proportion of pre-hypertensive and hypertensive children increased monotonically from 5 to 33%. This classification modes was tested on 66 children that were not originally included with a success rate of 97%. This predictive model will facilitate future longitudinal studies of obesity in children and will help plan interventions and evaluations of their results. This article is categorized under: Algorithmic Development > Biological Data Mining (AU)

• Processo FAPESP: 15/50064-0 - Avaliação de tecnologia on-line para prevenção primária de DCNT em usuários de serviços básicos de saúde do SUS
• Beneficiário: Daniel Gustavo Goroso
• Modalidade de apoio: Auxílio à Pesquisa - Pesquisa em Políticas Públicas
• Processo FAPESP: 17/09881-0 - Suporte técnico para atualização da plataforma FLEEM System
• Beneficiário: William Tsutomu Watanabe
• Modalidade de apoio: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico
• Processo FAPESP: 17/17434-4 - Avaliar a qualidade de vida e satisfação dos sujeitos participantes do estudo
• Beneficiário: Bruno Mendes de Oliveira Silva
• Modalidade de apoio: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico