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Pakiza Amin, Dr. Saima Gulzar Ahmad, Dr. Hikmat Ullah Khan, Dr. Ehsan Ullah Munir and Dr. Naeem Ramzan have published a research paper in the International Journal of Computational Intelligence Systems

Pakiza Amin, Dr. Saima Gulzar Ahmad, Dr. Hikmat Ullah Khan, Dr. Ehsan Ullah Munir and Dr. Naeem Ramzan have published a research paper titled “Predictive Analytics in Maternal Health: A Machine Learning Approach for Classification of Preeclampsia” in the International Journal of Computational Intelligence Systems (Vol. 18, 2025; ISSN: 1875-6883; Impact Factor ˜ 2.5).

This study applies advanced machine-learning techniques to the challenge of early detection of Preeclampsia — a serious hypertensive disorder in pregnancy. The authors utilized three datasets (including a local hospital clinical dataset plus publicly-available Mendeley and Kaggle datasets) and developed ensemble models (soft-voting, stacking, hybrid soft-stacking) to classify preeclampsia. Their models achieved high performance with AUC-ROC values exceeding 95% (and ~96% in the clinical dataset).