[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124749-en":3,"doc-seo-124749-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124749,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Detection of iron deficiency anemia by medical images - a comparative study of machine learning algorithms","Anemia represents a major global public health challenge affecting children and pregnant women, driven largely by iron deficiency. Red blood cell counts and Hb levels below normal thresholds indicate disease mechanisms such as increased destruction, blood loss, impaired production, or depleted red cell availability. This study develops and evaluates machine learning models using preprocessed, augmented palm images, comparing CNN, k-NN, Naïve Bayes, SVM, and Decision Tree for non-invasive detection performance.","Appiahene etal. BioData Mining (2023) 16:2 [https://doi.org/10.1186/s13040-023-00319-z](https://doi.org/10.1186/s13040-023-00319-z)  \nBioData Mining  \nRESEARCH Open Access  \nDetection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms  \nPeter Appiahene1*, Justice Williams Asare1, Emmanuel Timmy Donkoh2, Giovanni Dimauro3 and Rosalia Maglietta4  \n*Correspondence: [peter.appiahene@uenr.edu.gh](peter.appiahene@uenr.edu.gh)  \n1 Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana  \n2 Department of Basic and Applied Biology, University of Energy and Natural Resources, Sunyani, Ghana  \n3 Coordinatore del Consiglio Di Interclasse Dei Corsi Di Studio in InformaticaDipartimento Di Informatica, Università Degli Studi Di Bari ‘Aldo Moro’, Bari, Italy  \n4 Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing, National Research Council, Bari, Italy  \nAbstract  \nBackground: Anemia is one of the global public health problems that affect children and pregnant women. Anemia occurs when the level of red blood cells within the body decreases or when the structure of the red blood cells is destroyed or when the Hb level in the red blood cell is below the normal threshold, which results from one or more increased red cell destructions, blood loss, defective cell production or a depleted sum of Red Blood Cells.  \nMethods: The method used in this study is divided into three phases: the data  \nsets were gathered, which is the palm, pre-processed the image, which comprised; Extracted images, and augmented images, segmented the Region of Interest of the images and acquired their various components of the CIE L*a*b* colour space (also referred to as the CIELAB), and finally developed the proposed models for the detection of anemia using the various algorithms, which include CNN, k-NN, Nave Bayes, SVM, and Decision Tree. The experiment utilized 527 initial datasets, rotation, flipping and translation were utilized and augmented the dataset to 2635. We randomly divided the augmented dataset into 70%, 10%, and 20% and trained, validated and tested the models respectively.  \nResults: The results of the study justify that the models performed appropriately when the palm is used to detect anemia, with the Naïve Bayes achieving a 99. 96% accuracy while the SVM achieved the lowest accuracy of 96.34%, as the CNN also performed better with an accuracy of 99 . 92% in detecting anemia.  \nConclusions: The invasive method of detecting anemia is expensive and time-consuming; however, anemia can be detected through the use of non-invasive methods such as machine learning algorithms which is efficient, cost-effective and takes less time. In this work, we compared machine learning models such as CNN, k-NN, Decision Tree, Naïve Bayes, and SVM to detect anemia using images of the palm. Finally, the study supports other similar studies on the potency of the Machine Learning Algorithm as a non-invasive method in detecting iron deficiency anemia.  \nKeywords: Anemia, Image Augmentation, Machine learning algorithms, Red blood cell, Palpable palm, Region of Interest, Non-invasive  \n© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright","cbCaidGOjzJef3OC","https://ap.wps.com/l/cbCaidGOjzJef3OC","pdf",1828691,1,20,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# Keywords\n# Background\n## Global impact of anemia\n## Mechanisms and early detection\n## Symptoms and risk factors","[{\"question\":\"Why is detecting iron deficiency anemia important?\",\"answer\":\"Anemia impacts children and pregnant women and can lead to irreversible organ damage when not detected early.\"},{\"question\":\"Which machine learning algorithms are compared in the study?\",\"answer\":\"The study compares CNN, k-NN, Naïve Bayes, SVM, and Decision Tree for detecting iron deficiency anemia from palm images.\"},{\"question\":\"How is the dataset prepared and used for model training?\",\"answer\":\"Initial palm images are extracted, preprocessed, augmented, segmented for region of interest, converted into CIE L*a*b* color space components, and then split into training, validation, and testing sets.\"}]","Detection of iron deficiency anemia by medical images - 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