loading page

Iron Deficiency Anemia Detection using Machine Learning Models: A Comparative Study of Fingernails, Palm and Conjunctiva of the Eye Images.
  • +1
  • Justice Williams Asare,
  • Emmanuel Timmy Donkoh,
  • Giovanni Dimauro
Justice Williams Asare
University of Energy and Natural Resources

Corresponding Author:[email protected]

Author Profile
University of Energy and Natural Resources
Author Profile
Emmanuel Timmy Donkoh
University of Energy and Natural Resources
Author Profile
Giovanni Dimauro
Università degli Studi di Bari Aldo Moro
Author Profile


Anemia is one of the global public health challenges that particularly affect children and pregnant women. A study by WHO indicates that 42% of children below 6 years and 40% of pregnant women worldwide are anemic. This affects the world’s total population by 33%, due to the cause of iron deficiency. The non-invasive technique, such as the use of machine learning algorithms, is one of the methods used in the diagnosing or detection of clinical diseases, which anemia detection cannot be overlooked in recent days. In this study, machine learning algorithms were used to detect iron-deficiency anemia with the application of Naïve Bayes, CNN, SVM, k-NN, and Decision Tree. This enabled us to compare the conjunctiva of the eyes, the palpable palm, and the colour of the fingernail images to justify which of them has a higher accuracy for detecting anemia in children. The technique utilized in this study was categorized into three different stages: collecting of datasets (conjunctiva of the eyes, fingernails and the palpable palm images), preprocessing the images; image extraction, segmentation of the Region of Interest of the images, obtained each component of the CIE L*a*b* colour space (CIELAB). The models were then developed for the detection of anemia using various algorithms. The CNN had an accuracy of 99.12% in the detection of anemia, followed by the Naïve Bayes with an accuracy of 98.96%, while Decision Tree and k-NN had 98.29% and 98.92% accuracy respectively. However, the SVM had the least accuracy of 95.4% on the palpable palm. The performance of the models justifies that the non-invasive approach is an effective mechanism for anemia detection. Keywords: Iron deficiency, anemia, non-invasive, machine learning, data augmentation, algorithms, region of interest.
03 Feb 2023Submitted to Engineering Reports
06 Feb 2023Submission Checks Completed
06 Feb 2023Assigned to Editor
07 Feb 2023Review(s) Completed, Editorial Evaluation Pending
08 Feb 2023Reviewer(s) Assigned
15 Mar 2023Editorial Decision: Revise Major
22 Mar 20231st Revision Received
23 Mar 2023Submission Checks Completed
23 Mar 2023Assigned to Editor
23 Mar 2023Review(s) Completed, Editorial Evaluation Pending
24 Mar 2023Reviewer(s) Assigned
17 Apr 2023Editorial Decision: Revise Minor
18 Apr 20232nd Revision Received
18 Apr 2023Submission Checks Completed
18 Apr 2023Assigned to Editor
18 Apr 2023Review(s) Completed, Editorial Evaluation Pending
19 Apr 2023Editorial Decision: Accept