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A Performance Evaluation of Systematic Analysis for Combining Multi-class Models for Sickle Cell Disorder Data Sets

  • Mohammed Khalaf
  • Abir Jaafar Hussain
  • Dhiya Al-Jumeily
  • Robert Keight
  • Russell Keenan
  • Ala S. Al Kafri
  • Carl Chalmers
  • Paul Fergus
  • Ibrahim Olatunji Idowu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10362)

Abstract

Machine learning approach is considered as a field of science aiming specifically to extract knowledge from the data sets. The main aim of this study is to provide a sophisticate model to difference applications of machine learning models for medically related problems. We attempt for classifying the amount of medications for each patient with Sickle Cell disorder. We present a new technique to combine two classifiers between the Levenberg-Marquartdt training algorithm and the k-nearest neighbours algorithm. In this paper, we introduce multi-class label classification problem in order to obtain training and testing methods for each models along with other performance evaluations. In machine learning, the models utilise a training sets in association with building a classifier that provide a reliable classification. This research discusses different aspects of machine learning approaches for the classification of biomedical data. We are mainly focus on the multi-class label classification problem where many number of classes are available in the data sets. Results have indicated that for the machine learning models tested, the combination classifiers were found to yield considerably better results over the range of performance measures that been selected for this research.

Keywords

Machine-learning classifiers Sickle cell disorder SCD date sets Accuracy Performance evaluation 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mohammed Khalaf
    • 1
    • 2
  • Abir Jaafar Hussain
    • 1
  • Dhiya Al-Jumeily
    • 1
  • Robert Keight
    • 1
  • Russell Keenan
    • 3
  • Ala S. Al Kafri
    • 1
  • Carl Chalmers
    • 1
  • Paul Fergus
    • 1
  • Ibrahim Olatunji Idowu
    • 1
  1. 1.Faculty of Engineering and TechnologyLiverpool John Moores UniversityLiverpoolUK
  2. 2.Ministery of Higher Education and Scientific ResearchBagdad, Al-Rusafa RegionIraq
  3. 3.Liverpool Paediatric Haemophilia Centre, Haematology Treatment CentreAlder Hey Children’s HospitalLiverpoolUK

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