[Dissertation] Unsupervised Anomaly Detection of Healthcare Providers using Generative Adversarial Network
Masters thesis by Krishnan Naidoo, Faculty of Engineering, Built Environment and Information Technology University of Pretoria, Pretoria
Members
Krishnan Naidoo, MITC Big Data Science
Supervisor(s)
Dr. V. Marivate
Abstract
Available or adequate information to inform decision making for resource allocation in support of school improvement is a critical issue globally. In this paper, we apply machine learning and education data mining techniques on education big data to identify determinants of high schools’ performance in two African countries: South Africa and Sierra Leone. The research objective is to build predictors for school performance and extract the importance of different community-level and school-level features. We deploy interpretable metrics from machine learning approaches such as SHAP values on tree models and Logistic Regression odds ratios to extract interactions of factors that can support policy decision making. Determinants of performance vary in these two countries, hence different policy implications and resource allocation recommendations.
Publications
- Naidoo, K. and Marivate, V., 2020, April. Unsupervised anomaly detection of healthcare providers using generative adversarial networks. In Conference on e-Business, e-Services and e-Society (pp. 419-430). Springer, Cham.[Link]