Machine Learning-Based Early Detection of Anemia  Using Medical Sample Analysis

Author(s)	Prof. Harshith V, Chiranjeevi A, Chiranth C V, Disha H Thakur
Country	India
Abstract	Anemia, or a decrease in hemoglobin or red blood cells, plagues almost one-third of the world's population and is dangerous for health if not diagnosed. Traditional diagnosis, like complete blood counts and smear examination, though accurate, are invasive, time-consuming, and costly. Anemia detection techniques that are affordable, automated, and scalable have been made possible by advances in machine learning and deep learning. This paper discusses the various clinical data-driven methods, non-invasive image-based models, and ensemble learning methods for their accuracy, performance, and limitation, and their applicability in forming efficient systems for early detection and improved health outcomes.
Keywords	Anemia detection, Hemoglobin estimation, Red blood cells, Machine learning, Deep learning, Clinical data-driven models, Non-invasive diagnostics, Image-based analysis
Field	Medical / Pharmacy
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-15
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.60563

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