Precise Prediction of Total Body Lean and Fat Mass From Anthropometric and Demographic Data: Development and Validation of Neural Network Models | Zendy

Simon Lebech Cichosz | Zendy; Nicklas Højgaard Rasmussen | Zendy; Peter Vestergaard | Zendy; Ole Hejlesen | Zendy

Open Access

Precise Prediction of Total Body Lean and Fat Mass From Anthropometric and Demographic Data: Development and Validation of Neural Network Models

Author(s) -

Simon Lebech Cichosz,

Nicklas Højgaard Rasmussen,

Peter Vestergaard,

Ole Hejlesen

Publication year - 2020

Publication title -

journal of diabetes science and technology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.039

H-Index - 75

eISSN - 1932-3107

pISSN - 1932-2968

DOI - 10.1177/1932296820971348

Subject(s) - anthropometry , lean body mass , medicine , body fat percentage , pearson product moment correlation coefficient , population , trunk , dual energy x ray absorptiometry , body mass index , correlation coefficient , artificial neural network , statistics , mathematics , machine learning , computer science , body weight , bone mineral , biology , environmental health , ecology , osteoporosis

Estimating body composition is relevant in diabetes disease management, such as drug administration and risk assessment of morbidity/mortality. It is unclear how machine learning algorithms could improve easily obtainable body muscle and fat estimates. The objective was to develop and validate machine learning algorithms (neural networks) for precise prediction of body composition based on anthropometric and demographic data.

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