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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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Acknowledgments

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

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Correspondence to Mohammed Khalaf .

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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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