Data Science Frontiers

Research Seminar in Data Science - Rwanda

The seminar series is focused on both applied and theoretical aspects of data science. On the one hand, it is a place to exchange insights on developments in research and research methodologies; on the other hand, it is a place to discuss how to apply those techniques in order to address development challenges. In particular, talks focus on the following areas:

  • Applications of machine learning and artificial intelligence

  • Mathematical approaches to machine learning

  • Topics in algorithm development and design

  • Applications of statistics, economics, business, computer science, and engineering using big data and data analytics

  • Quantum information and related topics

We welcome suggestions for speakers concerning new and exciting developments in both theoretical and applied aspects of data science. If you would like to suggest a speaker, or give us feedback, please get in touch with Molly Mutesi at

Next Event

Friday, October 14

2pm CAT

Similien Ndagijimana



Introduction: Rwanda reported the rate at 33% in 2020. Globally, deaths from malnutrition

stand at 45% of child deaths. According to the World Health Organization's (WHO) goal to

reduce malnutrition by 3.9% per year, all countries must define appropriate strategies. Data

science and machine learning are currently important contributors to predicting and assessing the

outcomes of patients. Hence, developing a model capable of predicting stunting in Rwandan

children is critical, so that the best options for early diagnosis or detection of stunting, as well as

treatment, can be made a policy priority for the community and health services.

Methods: The Rwanda Demographic and Health Survey (RDHS) 2019-2020 was utilized as

secondary data. Using stratified 10-fold cross-validation, different Machine learning classifiers

were trained to predict stunting status. The prediction models were compared using a variety of

metrics, including accuracy, sensitivity, specificity, F1 score, and AUC, and the best model was


Results: The Gradient Boosting Classifier developed the best model, revealing that the training

accuracy was 80.49 % based on the performance indicators of several models. Based on the

confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the

model's ability to classify stunting cases correctly at 79.33%, identify stunted children correctly

at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve

of 0.89. The model found that the mother's height, television watching, the child's age, province,

mother education, birth weight childbirth size were the most important predictors of stunting


Recommendation: Therefore, machine-learning techniques may be used not only in Rwanda but

also worldwide to construct an accurate model that can detect the early stages of stunting and

offer the best predictive attributes to help in the prevention and control of stunting in Rwandan

children under the age of five.

Mailing List

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Seminars are held online, the presentations are recorded and videos are made available upon request. If you want to access a recording of one of the seminars, please get in touch with Molly Mutesi at . A list of past seminars can be found here, including the talk slides in case the speakers have shared them. All seminars, unless otherwise stated, are held ever second Friday at 14:00 CAT. The invitation will be shared via email. To receive the link, please sign up to our mailing list above.

Organizing Committee

Charles Nzaramyimana (ACE-DS, University of Rwanda) Email:

Franca Hoffmann (QLA, AIMS-Carnegie Research Chair in Data Science; Hausdorff Center for Mathematics, University of Bonn, Professor) Email:

Kabano Ignace (ACE-DS, University of Rwanda) Email:

Molly Mutesi (QLA, AIMS, Project Coordinator) Email:

Chanelle Matadah Manfouo (QLA, AIMS, PhD Student) Email:

Jennifer Batamuliza (ACE-DS, University of Rwanda, PhD Student) Email:

Muhammed Semakula (University of Rwanda, PhD Student) Email: