Analysis of Anemia Detection from Images of Eye Conjunctiva Using Machine Learning Algorithms

DOI

10.2991/978-94-6463-718-2_91How to use a DOI?

Keywords

Machine Learning; Anaemia; SVM; Early Detection; conjunctiva

Abstract

Anemia, which is a common medical condition seen in many parts of the world with classic manifestations of fatigue, Weakness and dyspnea. Early detection of Anemia helps to prevent its progression and timely treatment. The dataset used in this study contained 218 images of conjunctiva, a good indicator of clinical signs of anemia, and was extracted from eye-defy dataset found in IEEE data port. The study found the application of machine learning classifiers with supporting data, especially the optimized SVM with SMOTE, gives the better classification of clinical symptoms of anemia. Accuracy of this proposed method improved to 94% post-optimization. In addition, a feedback mechanism has been implemented to improve the accuracy of the model. Through this system, physicians were able to provide feedback on predictions made by the model, which could be used to retrain the model, thereby reducing misinferences. By utilizing an RGB camera and machine learning algorithms, the proposed system has the potential to provide an effective and efficient solution for anemia detection, enabling timely treatment and improved patient outcomes. The focus of the study is on improving the accuracy and reliability of the design, as well as integrating optimization methods and feedback loops. Such an investigation can stimulate future research and implementation in the field of machine learning and medicine.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - K. Uma
AU  - R. Parvathi
AU  - Poorva Agrawal
AU  - Aaditri Mittal
PY  - 2025
DA  - 2025/05/23
TI  - Analysis of Anemia Detection from Images of Eye Conjunctiva Using Machine Learning Algorithms
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1084
EP  - 1095
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_91
DO  - 10.2991/978-94-6463-718-2_91
ID  - Uma2025
ER  -