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International Conference on Intelligent Computing

ICIC 2019: Intelligent Computing Theories and Application pp 580–591Cite as

An Application of Using Support Vector Machine Based on Classification Technique for Predicting Medical Data Sets

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11644)

Abstract

This paper illustrates the utilise of various kind of machine learning approaches based on support vector machines for classifying Sickle Cell Disease data set. It has demonstrated that support vector machines generate an essential enhancement when applied for the pre-processing of clinical time-series data set. In this aspect, the objective of this study is to present discoveries for a number of classes of approaches for therapeutically associated problems in the purpose of acquiring high accuracy and performance. The primary case in this study includes classifying the dosage necessary for each patient individually. We applied a number of support vector machines to examine sickle cell data set based on the performance evaluation metrics. The result collected from a number of models have indicated that, support vector Classifier demonstrated inferior outcomes in comparison to Radial Basis Support Vector Classifier. For our Sickle cell data sets, it was found that the Parzen Kernel Support Vector Classifier produced the highest levels of performance and accuracy during training procedure accuracy 0.89733, AUC 0.94267. Where the testing set process, accuracy 0.81778, the area under the curve with 0.86556.

Keywords

  • Machine learning
  • Support vector machines
  • Sickle cell disorder data set
  • Evaluation techniques
  • Classification

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  • DOI: 10.1007/978-3-030-26969-2_55
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Acknowledgments

The authors would like to thank Al-Maarif University College for supporting this research.

Author information

Authors and Affiliations

  1. Department of Computer Science, Al-Maarif University College, Ramadi, Anbar, 31001, Iraq

    Mohammed Khalaf, Mahmood Alsaadi & Dhafar Hamed Abd

  2. Faculty of Engineering and Technology, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK

    Abir Jaafar Hussain & Dhiya Al-Jumeily

  3. College of Technological Innovation, Zayed University, Abu Dhabi, UAE

    Omar Alafandi

  4. Abu Dhabi Health Services Company (SEHA), Sultan Bin Zayed Street, PO Box 109090, Abu Dhabi, United Arab Emirates

    Mohamed Alloghani

  5. College of Computer Science and Information Technology, University of Anbar, Ramadi, 31001, Iraq

    Omar A. Dawood

Authors
  1. Mohammed Khalaf
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  2. Abir Jaafar Hussain
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  4. Dhiya Al-Jumeily
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  8. Dhafar Hamed Abd
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Corresponding author

Correspondence to Mohammed Khalaf .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Nanchang Institute of Technology, Nanchang, China

    Zhi-Kai Huang

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Khalaf, M. et al. (2019). An Application of Using Support Vector Machine Based on Classification Technique for Predicting Medical Data Sets. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_55

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  • DOI: https://doi.org/10.1007/978-3-030-26969-2_55

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