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Medical data mining in sentiment analysis based on optimized swarm search feature selection

  • Daohui Zeng
  • Jidong Peng
  • Simon Fong
  • Yining Qiu
  • Raymond Wong
Special Issue Article
  • 70 Downloads

Abstract

In this paper, we propose a novel technique termed as optimized swarm search-based feature selection (OS-FS), which is a swarm-type of searching function that selects an ideal subset of features for enhanced classification accuracy. In terms of gaining insights from unstructured medical based texts, sentiment prediction is becoming an increasingly crucial machine learning technique. In fact, due to its robustness and accuracy, it recently gained popularity in the medical industries. Medical text mining is well known as a fundamental data analytic for sentiment prediction. To form a high-dimensional sparse matrix, a popular preprocessing step in text mining is employed to transform medical text strings to word vectors. However, such a sparse matrix poses problems to the induction of accurate sentiment prediction model. The swarm search in our proposed OS-FS can be optimized by a new feature evaluation technique called clustering-by-coefficient-of-variation. In order to find a subset of features from all the original features from the sparse matrix, this type of feature selection has been a commonly utilized dimensionality reduction technique, and has the capability to improve accuracy of the prediction model. We implement this method based on a case scenario where 279 medical articles related to ‘meaningful use functionalities on health care quality, safety, and efficiency’ from a systematic review of previous medical IT literature. For this medical text mining, a multi-class of sentiments, positive, mixed-positive, neutral and negative is recognized from the document contents. Our experimental results demonstrate the superiority of OS-FS over traditional feature selection methods in literature.

Keywords

Medical text mining Optimized swarm search-based feature selection Sentiment prediction Clustering-by-coefficient-of-variation 

Notes

Acknowledgements

This paper is supported by the research grant “Temporal Data Stream Mining by Using Incrementally Optimized Very Fast Decision Forest (iOVFDF),” Grant No. MYRG2015-00128-FST, which is offered by the University of Macau, FST, and RDAO.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ehtical approval

This article does not contain any studies with human participants and animals performed by any of the authors.

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Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2018

Authors and Affiliations

  • Daohui Zeng
    • 1
  • Jidong Peng
    • 2
  • Simon Fong
    • 3
  • Yining Qiu
    • 4
  • Raymond Wong
    • 4
  1. 1.First Affiliated Hospital of Guangzhou University of TCMGuangzhouPeople’s Republic of China
  2. 2.Ganzhou People’s HospitalJiangxiPeople’s Republic of China
  3. 3.Department of Computer and Information ScienceUniversity of MacauTaipaPeople’s Republic of China
  4. 4.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia

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