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Multimedia Tools and Applications

, Volume 77, Issue 3, pp 3729–3743 | Cite as

Simultaneously aided diagnosis model for outpatient departments via healthcare big data analytics

  • Ying Hu
  • Kui Duan
  • Yin Zhang
  • M. Shamim Hossain
  • Sk Md Mizanur Rahman
  • Abdulhameed Alelaiwi
Article

Abstract

Recent real medical datasets show that the number of outpatients in China has sharply increased since 2013, when the Chinese health insurance reform started. This situation leads to increased waiting time for the outpatients; in particular, the normal operation of a hospital will be congested at rush hour. The existence of this problem in outpatient departments causes a reduction in doctors’ diagnostic time, and a high working strength is required to address this issue. In this paper, a simultaneous model based on machine learning is proposed for aiding outpatient doctors in performing diagnoses. We use Support Vector Machine (SVM) and Neural Networks (NN) to classify hyperlipemia using the clinical features extracted from a real medical dataset. The results, with an accuracy of 90 %, indicate that our Simultaneously Aided Diagnosis Model (SADM) applied to aid diagnosis for outpatient doctors and achieves the objective of increasing efficiency and reducing working strength.

Keywords

Machine learning Aided diagnosis SVM Spatio-temporal evolution 

Notes

Acknowledgments

The authors would like to extend their sincere appreciations to the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia for its funding of this research through the Profile Research Group project (PRG-1436-17).

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Ying Hu
    • 1
  • Kui Duan
    • 2
  • Yin Zhang
    • 3
  • M. Shamim Hossain
    • 4
  • Sk Md Mizanur Rahman
    • 5
  • Abdulhameed Alelaiwi
    • 4
  1. 1.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
  2. 2.School HospitalHuazhong University of Science and TechnologyWuhanChina
  3. 3.School of Information and Safety EngineeringZhongnan University of Economics and LawWuhanChina
  4. 4.Software Engineering Department, College of Computer and Information ScienceKing Saud UniversityRiyadhSaudi Arabia
  5. 5.Information Systems Department, College of Computer and Information ScienceKing Saud UniversityRiyadhSaudi Arabia

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