A data-driven approach to predict Small-for-Gestational-Age infants

Jingchao Sun, Lu Liu, Jianqiang Li, Ji Jiang Yang, Shi Chen, Qing Wang, Mengchu Zhou, Rong Lia, Bo Liu, Jing Bi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This work studies the problem of identifying risk factors of Small for Gestational Age (SGA) and building classifiers for SGA prediction. Recently, SGA infants have received more and more concerns as this illness brings many difficulties to them along with their whole life. Some experts have begun to study the risk factors of SGA onset by using traditional statistical ways. Others have used logistic regression (LR) to construct SGA prediction models. Meanwhile, machine learning have evolved and envisioned as a tool able to potentially identify babies with SGA. This work tests several feature selection methods. Based on the risk factors obtained through them, it trains support vector machine, random forest, and LR models and evaluates them via 10-fold cross validation in terms of precision and area under the curve of receiver operator characteristic curve. The results show that sparse LR of the wrapper algorithms owns the best feature selection effectiveness. In addition, this work compares data driven factors and knowledge driven factors and shows that the feature selection is necessary and effective. Among the trained classifiers, the LR model achieves the best performance on the data driven factors.

Original languageEnglish (US)
Title of host publicationICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467399753
DOIs
StatePublished - May 25 2016
Event13th IEEE International Conference on Networking, Sensing and Control, ICNSC 2016 - Mexico City, Mexico
Duration: Apr 28 2016Apr 30 2016

Publication series

NameICNSC 2016 - 13th IEEE International Conference on Networking, Sensing and Control

Other

Other13th IEEE International Conference on Networking, Sensing and Control, ICNSC 2016
Country/TerritoryMexico
CityMexico City
Period4/28/164/30/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Control and Systems Engineering
  • Modeling and Simulation

Keywords

  • Classification
  • feature selection
  • machine learning
  • prediction model
  • small for gestational age

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