Issue |
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
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Article Number | 04006 | |
Number of page(s) | 5 | |
Section | AI and Advanced Applications | |
DOI | https://doi.org/10.1051/itmconf/20257004006 | |
Published online | 23 January 2025 |
Analyzing the Application of Machine Learning in Anemia Prediction
Faculty of innovation Engineering, Macau University of Science and Technology, Chongqing, 402167, China
Corresponding author: 1230005529@student.must.edu.mo
This paper explores the applications of machine learning in the prediction of anemia, highlighting its potential to revolutionize clinical diagnosis and management. Anemia, a prevalent condition affecting millions globally, is often underdiagnosed due to traditional diagnostic methods that rely on clinical judgment and standard laboratory tests. Machine learning techniques provide innovative solutions by analyzing complex datasets that incorporate questionnaire, clinical features, demographic information, and laboratory results, thereby enhancing the accuracy of anemia predictions. This paper examines decision trees, random forests, support x'ector machines, and neural networks. emphasizing their efficacy in identifying patterns and risk factors associated with anemia. Obstacles such as data quality, feature selection, and model interpretability continue to hinder clinical adoption. The review identifies future research directions aimed at improving model generalizability and interpretability, ensuring that these technologies can be effectively integrated into healthcare practice. This paper advocates for the systematic adoption of machine learning methodologies in anemia management, positing that such innovations are crucial for advancing public health and optimizing resource allocation in clinical settings.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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