Performance evaluation of artificial neural network and multiple linear regression in the prediction of body mass index in children

Author:

Asif MuhammadORCID,Khosa Ghazi Khan,Alomair Abdullah Mohammad,Alomair Mohammad Ahmed,Aslam Muhammad,Arslan Muhammad,Sanaullah Muhammad,Wyszyńska Justyna

Abstract

AbstractThe body mass index (BMI) provides essential medical information related to body weight for the treatment and prognosis prediction of different diseases. The main goal of the present study was to evaluate the performance of artificial neural network (ANN) and multiple linear regression (MLR) model in the prediction of BMI in children. The data from a total of 5,964 children aged 5 to 12 years were included in study. Age, gender, neck circumference (NC), waist circumference (WC), hip circumference (HpC), and mid upper arm circumference (MUAC) measurements were used to estimate the BMI of children. The ANN and MLR were utilized to predict the BMI. The predictive performance of these methods was also evaluated. Gender-wise average comparison showed that median values of all the anthropometric measurements (except BMI) were significantly higher in boys as compared to girls. For the overall sample, the BMI prediction model was,― 0.147 X Age ― 0.367 X Gender + 0.176 X NC + 0.041 X WC + 0.060 X HpC + 0.404 X MUAC. A high R2value and lower RMSE, MAPE, and MAD indicated that the ANN is the best method for predicting BMI in children. Our results confirm that the BMI of children can be predicted by using ANN and MLR regression methods. However, the ANN method has a higher predictive performance than MLR.

Publisher

Cold Spring Harbor Laboratory

Reference26 articles.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3