Identification of Thalassemia Using Support Vector Machine

Gurbinder Kaur, Vijay Kumar Garg

2025

Abstract

Thalassemia is an inherited blood disorder where a person does not have normal hemoglobin production and is due to abnormal genes present in the DNA of a person. Consanguineous marriages bring about an increase in the occurrence of such disorders especially in the Thai regions of the world thus, making it a serious public health issue. An early and precise diagnosis for this type of blood disorder is crucial for better patient management as well as providing genetic counseling effectively. This study focuses on the application of Support Vector Machine to identify thalassemia from CBC data. We aim to develop a reliable and robust model by integrating various hematological parameters including hemoglobin concentration, red blood cell indices and other Thalassemia relevant biomarkers. The concerned dataset was pre-treated to take care of the missing values and maximization of SVM efficacy was achieved by data normalization. It was found that the SVM model is quite efficient in terms of accuracy of classification, hence it can be considered as a good tool for diagnosis for Thalassemia. The SVM technique has the potential to make the identification, treatment expeditious and litheness and the scope of Thalassemia detection relatively large.

Paper Citation

in Harvard Style

Kaur G. and Garg V. (2025). Identification of Thalassemia Using Support Vector Machine. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 600-604. DOI: 10.5220/0013917500004919

in Bibtex Style

@conference{icrdicct`2525,
author={Gurbinder Kaur and Vijay Garg},
title={Identification of Thalassemia Using Support Vector Machine},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={600-604},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013917500004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Identification of Thalassemia Using Support Vector Machine
SN - 978-989-758-777-1
AU - Kaur G.
AU - Garg V.
PY - 2025
SP - 600
EP - 604
DO - 10.5220/0013917500004919
PB - SciTePress