A neural network analysis of Lifeways cross-generation imputed data
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|Title:||A neural network analysis of Lifeways cross-generation imputed data||Authors:||Kelly, Gabrielle E.||Permanent link:||http://hdl.handle.net/10197/10670||Date:||14-Dec-2018||Online since:||2019-05-27T14:24:40Z||Abstract:||Objectives: Neural networks are a powerful statistical tool that use nonlinear regression type models to obtain predictions. Their use in the Lifeways cross-generation study that examined body mass index (BMI) of children, among other measures, is explored here. Our aim is to predict the BMI of children from that of their parents and maternal and paternal grandparents. For comparison purposes, linear models will also be used for prediction. A complicating factor is the large amount of missing data. The missing data may be imputed and we examine the effects of different imputation methods on prediction. An analysis using neural networks (and also linear models) that uses all available data without imputation is also carried out, and is the gold standard by which the analyses with imputed data sets are compared. Results: Neural network models performed better than linear models and can be used as a data analytic tool to detect nonlinear and interaction effects. Using neural networks the BMI of a child can be predicted from family members to within roughly 2.84 units. Results between the imputation methods were similar in terms of mean squared error, as were results based on imputed data compared to un-imputed data.||Funding Details:||Health Research Board||Type of material:||Journal Article||Publisher:||BioMed Central||Journal:||BMC Research Notes||Volume:||11||Issue:||897||Start page:||1||End page:||6||Copyright (published version):||2018 the Authors||Keywords:||Body mass index; Child; Neural networks; Multiple imputation; Multiple imputation by chained equations; Principal components; Reduction method; Lifeways||DOI:||10.1186/s13104-018-4013-2||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Mathematics and Statistics Research Collection|
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