Twin Support Vector Machines for Thalassemia Classification

Alva Andhika Sa'Id, Zuherman Rustam, Fevi Novkaniza, Qisthina Syifa Setiawan, Faisa Maulidina, Velery Virgina Putri Wibowo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Thalassemia is one of the incurable blood disorders inherited from parents with its history. This disease causes abnormality in the blood cells, specifically the protein composition such as hemoglobin. Furthermore, it has spread out across the Mediterranean Sea and through Indonesia due to the migration of people. Early detection to diagnose thalassemia is necessary to prevent the disease from spreading to another generation. This study aims to analyze the impact of machine learning in medical diagnosis, and its disease detection methods based on clinical history. Several previous studies have been incorporated into early screening for diagnosis of thalassemia with machine learning technique based on classification problem, and it showed great performance evaluation beyond 90% accuracy. In addition, the data used was laboratory results of blood check obtained from Harapan Kita Children and Women's Hospital, Jakarta, Indonesia. Twin Support Vector Machines (TSVM) is used in this study as one of the machine learning developed techniques inspired by Support Vector Machines (SVM), as this technique purposed to find the nonparallel hyperplanes to solve binary classification problem. This was conducted through three commonly used kernels from several previous studies, including Linear, Polynomial, and Radial Basis Function (RBF). The results showed that RBF TSVM gave the best results with 99.32%, 99.75% and 99.24% average of accuracy, precision, and F1 score, respectively. However, Polynomial TSVM, as the lowest results had 99.79% average of recall. In this context, the TSVM role is recommended for future studies to facilitate medical diagnosis based on the clinical history of other diseases.

Original languageEnglish
Title of host publication2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages160-164
Number of pages5
ISBN (Electronic)9781665440325
DOIs
Publication statusPublished - 29 Sep 2021
Event2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021 - Virtual, Online, Bahrain
Duration: 29 Sep 202130 Sep 2021

Publication series

Name2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021

Conference

Conference2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021
Country/TerritoryBahrain
CityVirtual, Online
Period29/09/2130/09/21

Keywords

  • Classification
  • Kernel function
  • Machine Learning
  • Thalassemia
  • Twin Support Vector Machines

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