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

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 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.

Prediction of Anemia using various Ensemble Learning and Boosting Techniques

“…Their study showed that Random Forest worked best with accuracy of 86.4%. In the research work [11], ML algorithms were used to determine if conjunctiva of eyes, palpable palm or color of fingernails is more accurate to detect anemia among children. Their study showed that CNN has worked better, being 99.12% accurate.…”

Diffuse reflectance spectroscopy and RGB-imaging: a comparative study of non-invasive haemoglobin assessment

“…Compared to the conjunctiva and fundus, the region of ngernails appears to be the most accessible melanin-free tissue site, hence, recent works [20][21][22][23][24][25][26][27] have utilized RGB-imaging of ngernails for assessing Hb level. The work of Mannino et al demonstrated a root mean squared error of ~12 g/L and detection of Hb level lower than 125 g/L with a sensitivity and speci city values of 0.92 and 0.76, respectively.…”

“…The work of Mannino et al demonstrated a root mean squared error of ~12 g/L and detection of Hb level lower than 125 g/L with a sensitivity and speci city values of 0.92 and 0.76, respectively. Typically, further studies report similar or even higher regression and classi cation quality scores [21][22][23][24][25][26][27], which, however, are not always con rmed in independent prospective studies. For example, RGB-imaging method applied for estimation of low Hb level demonstrated sensitivity and speci city at the level of 0.4-0.6 [3].…”

Artificial Intelligence (AI)-Based Detection of Anaemia Using the Clinical Appearance of the Gingiva

“…Anaemia happens either when the body's red blood cell production declines or the cells' structural integrity is compromised [ 3 ]. Anaemia can also appear when the haemoglobin level in the red blood cells drops below the typical threshold as a result of increases in red blood cell oxidation, blood loss, defective cells, or a reduction in the quantity of red blood cells [ 1 ].…”