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SVM Based Predictive Model for SGA Detection

  • Haowen Mo
  • Jianqiang Li
  • Shi Chen
  • Hui Pan
  • Ji-Jiang YangEmail author
  • Qing Wang
  • Rui Mao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9677)

Abstract

The medical diagnosis process can be interpreted as a decision making process, which doctors determine whether a person is suffering from a disease based on the medical examination. This process can also be computerized in order to present medical diagnostic procedures in an accurate, objective, rational, and fast way. This paper presents a detection model for small for gestational age (SGA) based on support vector machine (SVM). For this purpose, a dataset was adopted from pregnancy eugenic investigation to train the classification model. Then empirical experiments were conducted for SGA detection. The results indicate that support vector machine is considerably effective to detect SGA to help doctors make the final diagnosis.

Keywords

Small for gestational age Support vector machine Classification Healthcare 

Notes

Acknowledgments

This work is supported by Beijing Natural Science Foundation (4152007), China National Key Technology Research and Development Program project with no. 2013BAH19F01 and Guangdong Key Laboratory of Popular High Performance Computers, Shenzhen Key Laboratory of Service Computing and Applications.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Haowen Mo
    • 1
    • 2
  • Jianqiang Li
    • 1
    • 2
  • Shi Chen
    • 3
  • Hui Pan
    • 3
  • Ji-Jiang Yang
    • 4
    Email author
  • Qing Wang
    • 4
  • Rui Mao
    • 2
  1. 1.School of Software EngineeringBeijing University of TechnologyBeijingChina
  2. 2.Guangdong Key Laboratory of Popular High Performance Computers, Shenzhen Key Laboratory of Service Computing and ApplicationsShenzhenChina
  3. 3.Department of EndocrinologyPeking Union Medical College HospitalBeijingChina
  4. 4.Tsinghua National Laboratory for Information Science and TechnologyTsinghua UniversityBeijingChina

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