article · BioData Mining
Anemia is a major global public health concern that primarily impacts pregnant women and children. Standard invasive diagnostic methods can be expensive and slow. To address this, non-invasive detection of iron deficiency anemia was assessed using palm images and machine learning techniques. The study processed an initial set of 527 palm images, expanding it through augmentation to 2,635 images. Regions of interest were extracted, segmented, and analysed using components of the CIE L*a*b* colour space. Several algorithms were trained, validated, and tested, including convolutional neural networks, k-nearest neighbours, Naive Bayes, support vector machines, and decision trees. Naive Bayes attained the highest diagnostic accuracy at 99.96 percent, with convolutional neural networks reaching 99.92 percent, and support vector machines recording the lowest accuracy at 96.34 percent. The findings confirm that machine learning analysis of palm imagery provides an efficient, low-cost approach to detecting anemia.
Traditional diagnostic tests for anemia rely on invasive blood draws that require laboratory infrastructure, making them costly and slow. Using palm photographs offers a rapid, non-invasive alternative for screening vulnerable groups such as children and pregnant women. High-accuracy computational models could expand diagnostic access in healthcare environments where blood-testing facilities are limited or unavailable.
This research could support non-invasive diagnostic software tools for healthcare workers or point-of-care screening devices using digital palm photographs. The technology is in the applied and tested research stage, having demonstrated very high accuracy on an augmented dataset of 2,635 images. Commercial deployment would require transition from experimental image sets to prospective clinical validation in operational healthcare settings.
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BACKGROUND: 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. METHODS: The method used in this study is divided into three phases: the datasets 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. RESULTS: 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. CONCLUSIONS: 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.
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DOI: 10.1186/s13040-023-00319-z
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