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Sandra Addai-Henne

Statistics and Actuarial Science

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Research Summary

(inferred from publications by AI)

The researcher's work integrates innovative approaches across multiple disciplines, combining advancements in machine learning for imbalanced data classification in fraud detection to study eating disorders post-childbirth with a focus on neonatal respiratory health research.

Research Themes

All Papers

A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions(2023)
Supervised Machine Learning Algorithm Approach to Detecting and Predicting Fraud in Credit Card Transactions(2022)
Body Image: a Cross-sectional Study of the Perception and Practices of Women After Delivery(2024)
Body image: a cross-sectional study of the perception and practices of women after delivery(2025)
Evaluating the Effectiveness of an Evidence-Based Practice in Neonatal Resuscitation among Birth Asphyxiated Newborns in a Developing Country(2024)

Collaboration Network

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About This Profile

This profile is generated from publicly available publication metadata and is intended for research discovery purposes. Themes, summaries, and trajectories are inferred computationally and may not capture the full scope of the lecturer's work. For authoritative information, please refer to the official KNUST profile.