Comprehensive Machine Learning Framework for the Early Detection and Analysis of Anemia Through Hemoglobin Level Assessment

DOI

10.2991/978-94-6463-754-0_4How to use a DOI?

Keywords

Machine learning; health care; anemia detection; logistic regression

Abstract

This new approach provides and includes complex architecture based on machine learning techniques for the early detection and diagnosis of anemia by calculating the concentration of hemoglobin in the patients’ blood. By using supervised learning techniques, the system endeavors to estimate the probability related to the presence of anemia using hemoglobin data, which gives immediate assessment and analysis of the severity of the condition. The ability of the system to identify early-stage anemia is useful for providing timely treatment and avoiding complications.

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  - G. Jeya Krishna
AU  - R. S. Sheshanth
AU  - N. Sri Ragavendiran
AU  - B. Aswin Kumar
AU  - M. Amsa
PY  - 2025
DA  - 2025/06/30
TI  - Comprehensive Machine Learning Framework for the Early Detection and Analysis of Anemia Through Hemoglobin Level Assessment
BT  - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
PB  - Atlantis Press
SP  - 25
EP  - 33
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-754-0_4
DO  - 10.2991/978-94-6463-754-0_4
ID  - Krishna2025
ER  -