A novel MOGA-SVM multinomial classification for organ inflammation detection

Kwok Tai Chui, Miltiadis D. Lytras

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

Wrist pulse signal (WPS) contains crucial information of humans' health condition. It can serve as an alternative method for diagnosing of organ inflammation instead of traditional clinical measurement. In this paper, a novel multi-objective genetic algorithm based support vector machine (MOGA-SVM) has been proposed for the multinomial classification of the inflammations of appendix, pancreas, and duodenum. A customized similarity kernel (KCS) has been optimally designed. The performance of multinomial classification using KCS is compared with five types of kernels, linear, radial basis function (RBF), polynomial and sigmoid kernel, as well as mixtures of polynomial and RBF, to verify the effectiveness of KCS. The sensitivity, specificity and accuracy (Acc) of the proposed method are 92%, 91.2%, and 91.6% respectively. The results have demonstrated that KCS improves the accuracy of classification from 8.9% to 59.6%. When compared to related work, the proposed method increases the performance by more than 10%. It is believed that WPS can serve as alternative measures to diagnose organ inflammations.

Original languageEnglish
Article number2284
JournalApplied Sciences (Switzerland)
Volume9
Issue number11
DOIs
Publication statusPublished - 1 Jun 2019
Externally publishedYes

Keywords

  • Bioinformations
  • Genetic algorithm
  • Multiobjective optimization
  • Organ inflammation
  • Support vector machine
  • Wrist pulse signal

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