Analyzing the Application of Machine Learning in Anemia Prediction
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 c...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04006.pdf |
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| _version_ | 1825206582012542976 |
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| author | Li Yuxi |
| author_facet | Li Yuxi |
| author_sort | Li Yuxi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9f95d04ff95647a4a7a95c8571d76ef1 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-9f95d04ff95647a4a7a95c8571d76ef12025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700400610.1051/itmconf/20257004006itmconf_dai2024_04006Analyzing the Application of Machine Learning in Anemia PredictionLi Yuxi0Faculty of innovation Engineering, Macau University of Science and TechnologyThis 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.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04006.pdf |
| spellingShingle | Li Yuxi Analyzing the Application of Machine Learning in Anemia Prediction ITM Web of Conferences |
| title | Analyzing the Application of Machine Learning in Anemia Prediction |
| title_full | Analyzing the Application of Machine Learning in Anemia Prediction |
| title_fullStr | Analyzing the Application of Machine Learning in Anemia Prediction |
| title_full_unstemmed | Analyzing the Application of Machine Learning in Anemia Prediction |
| title_short | Analyzing the Application of Machine Learning in Anemia Prediction |
| title_sort | analyzing the application of machine learning in anemia prediction |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04006.pdf |
| work_keys_str_mv | AT liyuxi analyzingtheapplicationofmachinelearninginanemiaprediction |