Comprehensive Machine Learning Framework for the Early Detection and Analysis of Anemia Through Hemoglobin Level Assessment
Corresponding Author
G. Jeya Krishna
Available Online 30 June 2025.
- 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.
Cite this article
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 -