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Chemical reaction optimization to disease diagnosis by optimizing hyper-planes classifiers

  • Somayeh Jalayeri
  • Majid Abdolrazzagh-NezhadEmail author
Methodologies and Application
  • 14 Downloads

Abstract

Early diagnosis of diseases can save and leads to survival. There are several diagnoses techniques which mostly consist of classification and optimization parts. Although these techniques have their specific advantages, they have their significant disadvantages such as sensitivity to the number of features (symptoms) and need to features selection, challenge to detect non-integrated regions of one class and high complexity of their progresses. In this paper to fill up the disadvantages, a novel classification is proposed to disease diagnosis by different numbers of hyper-planes classifiers (HPC) that divides medical data into adequate regions based on assigning binary codes to each region. The HPC can find useful relationships between the symptoms of the diseases by tagging each region with the suitable class label. To optimize the HPC’s coefficients and improve disease diagnosis, chemical reaction optimization (CRO) is adapted based on four reactions on HPC’s coefficients, which are coded as molecular structures. Different numbers of HPCs are performed, and their experimental results are compared together. The interesting point of the results is disease diagnosis error 0.000% by five hyper-planes for test data of all investigated medical data set. Also, the best-obtained results of the CRO-HPC are compared with the best outputs of more than 50 methods of disease diagnosis from the previous state-of-the-art literature. This comparison shows that CRO-HPC’s diagnosis errors can compete with the majority of the other diagnostic methods.

Keywords

Hyper-planes classifier Classification of medical data Chemical reaction optimization Disease diagnosis 

Notes

Compliance with ethical standards

Conflict of interest

The authors of the current manuscript declare that Somayeh Jalayeri is graduated student of Islamic Azad University—Birjand Branch that she affiliated to her university and Majid Abdolrazzagh-Nezhad is Assist. Prof. of Bozorgmehr University of Qaenat as the supervisor of the current research. Except the above-declared conflict interest, the authors claim that there is not any conflict of interest and the research was not founded any grant.

Human participants and/or animals rights

In cases of research involving human participants and/or animals, the article does not contain any studies with human participants and/or animals performed by any of the authors. The investigated medical datasets to disease diagnosis are extracted from UCI (the UC Irvine Machine Learning Repository) that their details are presented in Appendix I.

Informed consent

The authors declare that informed consent was obtained from all individual participants included in the research.

Supplementary material

500_2019_3869_MOESM1_ESM.docx (98 kb)
Supplementary material 1 (DOCX 99 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Engineering, Birjand BranchIslamic Azad UniversityBirjandIran
  2. 2.Department of Computer Engineering, Faculty of EngineeringBozorgmehr University of QaenatQaenIran

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