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\begin{document}

\title{Iron Deficiency Anemia Detection using Machine Learning Models: A
Comparative Study of Fingernails, Palm and Conjunctiva of the Eye
Images.}



\author[1]{Justice Williams Asare}%
\author[1]{PETER APPIAHENE}%
\author[1]{Emmanuel Timmy Donkoh}%
\author[2]{Giovanni Dimauro}%
\affil[1]{University of Energy and Natural Resources}%
\affil[2]{Università degli Studi di Bari Aldo Moro}%


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  \date{May 21, 2024}


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\begin{abstract}
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 Na\selectlanguage{ngerman}ïve 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.%
\end{abstract}\selectlanguage{ngerman}%



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