Maryam Majid: Machine Learning for Anemia Risk Prediction
Maryam Majid, Data Science Student at University of the Punjab, shared a post on LinkedIn about a recent article she and her colleagues co-authored, published in IEEE Xplore, adding:
“Excited to Share Our Research Publication in IEEE Xplore!
I am delighted to announce that our research paper, ‘Comparative Analysis of the Haemoglobin Level across Dietary and Menstrual Blood Flow in Women of Reproductive Age,’ has been published in IEEE Xplore as part of the 2026 5th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET 2026).
This research was a rewarding journey from collecting primary data through in-person surveys and laboratory haemoglobin testing, to data cleaning and preprocessing, performing statistical analyses, and finally developing a Random Forest machine learning model to predict anemia risk.
The study explores the relationship between dietary habits, menstrual blood flow, and haemoglobin levels among women of reproductive age, demonstrating how data-driven approaches can support healthcare research.
This publication reflects the collaborative effort of an incredible team.
I am sincerely grateful to my co-authors Areeba Noor, Rida Batool, and Khadija Shahzad, and our supervisor Prof. Dr. Faisal Bukhari, whose guidance and support were invaluable throughout this journey.
I’m grateful for this milestone and excited to continue exploring the intersection of Data Science, Machine Learning, and Healthcare Research.”
Title: Comparative Analysis of the Haemoglobin Level across Dietary and Menstrual Blood Flow in Women of Reproductive Age
Authors: Areeba Noor, Rida Batool, Maryam Majid, Khadija Shahzad, Faisal Bukhari
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