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Smart Anemia Diagnosis: A Non-Invasive Hemoglobin Optical Sense with AI-based WebApp for Early Detection & Prevention

International Journal of Electrical and Electronics Engineering
© 2026 by SSRG - IJEEE Journal
Volume 13 Issue 1
Year of Publication : 2026
Authors : Megha Chakole, Rutuja Saharkar, Shrutika Janbandhu, Sanjay Dorle, Roshan Umate
10.14445/23488379/IJEEE-V13I1P106
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How to Cite?

Megha Chakole, Rutuja Saharkar, Shrutika Janbandhu, Sanjay Dorle, Roshan Umate, "Smart Anemia Diagnosis: A Non-Invasive Hemoglobin Optical Sense with AI-based WebApp for Early Detection & Prevention," SSRG International Journal of Electrical and Electronics Engineering, vol. 13,  no. 1, pp. 47-59, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I1P106

Abstract:

Monitoring blood Hemoglobin (Hb) level is crucial for diagnosis, evaluation, and treatment of various illnesses. Such illnesses can be cured at early stages by maintaining good blood health. For average humans, Hemoglobin may not hold much importance, but for anemic patients, it is the key to life. Given the vital role of Hemoglobin, there is a need for an efficient mechanism to estimate the Hb levels regularly and to detect and prevent Anemia prevalence in patients. Although the researchers have developed various models for anemia detection, none of the studies proposed the essential solutions to prevent it. This paper provides insights into a non-invasive method for monitoring Hb levels cost-effectively and providing preventive measures using an ML Algorithm. To construct the non-invasive kit, an Arduino UNO and an optical sensor MAX30105 have been put to use. Additionally, a web-based application has been designed using Streamlit Community Cloud & GitHub to aid in the early detection of anemia and provide tailored prescriptions, including numerous medicines and daily-life remedies. The performance parameters of the ML (specifically RandomForest Classifier) model are 0.9868 precision, 0.9841 recall, 0.9852 F1-score, and 0.9998 AUC-ROC score. Such accurate systems may eventually give doctors access to real-time data, facilitating quicker diagnosis and treatment. To enable reproducibility, the code developed for this research has been made openly accessible.

Keywords:

Non-Invasive, Hemoglobin, Optical Sensor, Anemia, Streamlit, RandomForest Classifier, Machine Learning, Github, Streamlit Community Cloud.

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