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Non-Invasive Anemia Detection using Machine Learning: A Comparative Analysis

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Non-invasive screening methods can significantly improve anemia detection accuracy, offering quicker results.
K-nearest neighbor model achieved an accuracy of 99.88% using an 80-20 data split ratio across various regions of interest.
The study assessed three machine learning models—support vector machines, k-nearest neighbor, and random forest—for optimal accuracy.
Findings emphasize the potential of affordable anemia detection techniques, particularly in rural and underserved areas.

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Comparative analysis reveals k-NN achieves 99.88% accuracy in detecting anemia, highlighting effective non-invasive methods.

Aboul-Yazeed et al. (2025) studied this question.

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