article · Engineering Reports
Iron deficiency anaemia presents a widespread global health problem, notably impacting young children and pregnant women. To address the need for non-invasive diagnostic tools, machine learning models can analyse physical features to identify the condition. An evaluation was conducted comparing images of children's eye conjunctiva, palms, and fingernail colour using several algorithms, including convolutional neural networks, support vector machines, Naïve Bayes, k-nearest neighbours, and decision trees. The experimental process involved dataset collection, preprocessing, and model development. Testing demonstrated that machine learning successfully detected anaemia from these photographic sites. Among the evaluated algorithms, the convolutional neural network achieved the highest detection accuracy at 99.12 per cent, whereas the support vector machine recorded the lowest accuracy at 95.4 per cent. Overall, the findings confirm that image-based, non-invasive assessment offers an effective method for identifying anaemia.
Iron deficiency anaemia affects approximately one third of the global population, with young children and pregnant women facing the highest risks. Traditional diagnosis often relies on invasive blood tests, which can be difficult in community screening. Demonstrating that image analysis of visible body sites can accurately identify anaemia provides a painless, accessible alternative for detecting individuals in need of care.
This research demonstrates an applied, algorithm-level method for non-invasive anaemia detection that could be integrated into mobile health applications or diagnostic imaging software. Such tools would primarily serve community healthcare workers and clinicians screening paediatric patients. While the models achieved high accuracy in testing, the abstract does not indicate that clinical validation or commercial deployment has occurred, placing the technology at an early applied stage.
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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 the age of 6 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 diagnosis or detection of clinical diseases, which anemia detection cannot be overlooked in recent days. In this study, a machine learning approach was used to detect iron‐deficiency anemia with the application of Naïve Bayes, CNN, SVM, k‐NN, and decision tree algorithms. This enabled us to compare the conjunctiva of the eyes, the palpable palm, and the color of the fingernail images to justify which of them has a higher accuracy for detecting anemia in children. The method utilized was categorized into three different stages: dataset collection, dataset preprocessing, and model development for anemia detection. The CNN achieved a higher accuracy of 99.12%, while the SVM had the least accuracy of 95.4%. The performance of the models justifies that the non‐invasive approach is an effective mechanism for anemia detection.
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DOI: 10.1002/eng2.12667
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