Applied Intelligence and Computing
Non-invasive Way to Detect Anemia using Machine Learning
Authors: Shyamala Mathi, Furqan Budye, Samiksha Chikka, Ajaz Shaikh and Amir Mithagari
Publishing Date: 08-11-2024
ISBN: 978-81-955020-9-7
Abstract
The challenge of anemia significantly impacts children and pregnant women worldwide. In recent times, there has been growing interest in using machine learning algorithms to tackle this issue. In this study, two machine learning methods were employed to identify iron-deficiency anemia by analyzing images of the conjunctiva, showing promising results. The process involves collection of data and converting the color space from RGB to CIELAB in preprocessing and developing their models. So, the abstract talks a lot about critical considerations in this field, like dataset quality and the pesky issue of model interpretability. We need more research to refine and validate these models across diverse populations and healthcare settings. It's advocating for collaboration between data scientists, medical professionals, and policymakers. Using machine learning to facilitate non-invasive techniques for anemia detection is a new technique in healthcare. It's like an advancement in human life. It's all about revolutionizing the way we diagnose, manage, and monitor anemia. And as technology keeps progressing and we get more and more data, itis going to make millions of lives better. Early detection, personalized treatment, efficient healthcare delivery becomes easy. As a result of our efforts, we have designed a computerized screening test that is noninvasive, easy to use, and affordable. This innovative test is portable and user-friendly, making it accessible for widespread use in developing nations. By offering a practical substitute to invasive techniques for detecting anemia, this screening test has the potential to significantly improve the quality of life for individuals in these regions. Its simplicity and affordability make it a valuable tool in addressing healthcare challenges faced by populations in resource-limited settings. This advancement represents a step forward in providing essential healthcare solutions tailored to the needs of underserved communities.
Keywords
Iron deficiency, anemia, non-invasive, machine learning, image processing, detect, user friendly, healthcare.
Cite as
Shyamala Mathi, Furqan Budye, Samiksha Chikka, Ajaz Shaikh and Amir Mithagari, "Non-invasive Way to Detect Anemia using Machine Learning", In: Mukesh Saraswat and Rajani Kumari (eds), Applied Intelligence and Computing, SCRS, India, 2024, pp. 91-103. https://doi.org/10.56155/978-81-955020-9-7-11