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A Systematic Comparison and Evaluation of Supervised Machine Learning Classifiers Using Headache Dataset

  • Ahmed J. Aljaaf
  • Dhiya Al-Jumeily
  • Abir J. Hussain
  • Paul Fergus
  • Mohammed Al-Jumaily
  • Naeem Radi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9227)

Abstract

The massive growth of data volume within the healthcare sector pushes the current classical systems that were adapted to the limit. Recent studies have focused on the use of machine learning methods to develop healthcare systems to extract knowledge from data by means of analysing, mining, pattern recognition, classification and prediction. Our research study reviews and examines different supervised machine learning classifiers using headache dataset. Different statistical measures have been used to evaluate the performance of seven well-known classifiers. The experimental study indicated that Decision Tree classifier achieved a better overall performance, followed by Artificial Neural Network, Support Vector Machine and k-Nearest Neighbor. This would determine the most suitable classifier for developing a particular classification system that is capable of identifying primary headache disorders.

Keywords

Machine learning Performance analysis Primary headache 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ahmed J. Aljaaf
    • 1
  • Dhiya Al-Jumeily
    • 1
  • Abir J. Hussain
    • 1
  • Paul Fergus
    • 1
  • Mohammed Al-Jumaily
    • 2
  • Naeem Radi
    • 3
  1. 1.Applied Computing Research GroupLiverpool John Moores UniversityLiverpoolUK
  2. 2.Department of NeurosurgeryDr. Sulaiman Al Habib HospitalDubai Healthcare CityUAE
  3. 3.Al Khawarizmi International CollegeAbu DhabiUAE

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