An ensemble-based feature selection framework to select risk factors of childhood obesity for policy decision making

BMC Medical Informatics and Decision Making

Roberto Bilbao¹  Bart De Moor²  Gorana Nikolic²  Xi Shi²  Joseba Bidaurrazaga Van-Dierdonck³  Gorka Epelde⁴  Mónica Arrúe⁴ 

[1] Basque Foundation for Research and Innovation, Bilbao, Spain;Department of Electrical Engineering (ESAT), Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Kasteelpark Arenberg 10 - box 2446, 3001, Leuven, Belgium;Regional office of the Health Department, Basque Government, Bilbao, Spain;Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain;Biodonostia Health Research Institute, eHealth Group, Donostia-San Sebastián, Spain;

关键词: Feature selection;    Ensemble learning;    Childhood obesity;    Public health;    Policy decision making;   

DOI  :  10.1186/s12911-021-01580-0

来源: Springer

【 摘 要 】

BackgroundThe increasing prevalence of childhood obesity makes it essential to study the risk factors with a sample representative of the population covering more health topics for better preventive policies and interventions. It is aimed to develop an ensemble feature selection framework for large-scale data to identify risk factors of childhood obesity with good interpretability and clinical relevance.MethodsWe analyzed the data collected from 426,813 children under 18 during 2000–2019. A BMI above the 90th percentile for the children of the same age and gender was defined as overweight. An ensemble feature selection framework, Bagging-based Feature Selection framework integrating MapReduce (BFSMR), was proposed to identify risk factors. The framework comprises 5 models (filter with mutual information/SVM-RFE/Lasso/Ridge/Random Forest) from filter, wrapper, and embedded feature selection methods. Each feature selection model identified 10 variables based on variable importance. Considering accuracy, F-score, and model characteristics, the models were classified into 3 levels with different weights: Lasso/Ridge, Filter/SVM-RFE, and Random Forest. The voting strategy was applied to aggregate the selected features, with both feature weights and model weights taken into consideration. We compared our voting strategy with another two for selecting top-ranked features in terms of 6 dimensions of interpretability.ResultsOur method performed the best to select the features with good interpretability and clinical relevance. The top 10 features selected by BFSMR are age, sex, birth year, breastfeeding type, smoking habit and diet-related knowledge of both children and mothers, exercise, and Mother’s systolic blood pressure.ConclusionOur framework provides a solution for identifying a diverse and interpretable feature set without model bias from large-scale data, which can help identify risk factors of childhood obesity and potentially some other diseases for future interventions or policies.

【 授权许可】

CC BY