Integrating machine learning into DHIS2 to predict multidrug resistant tuberculosis: a case of the DHIS2's electronic case based surveillance system

Abstract

Multidrug-resistant tuberculosis (MDR-TB) remains a critical public health challenge in Uganda, where cases continue to rise. The existing electronic Case-Based Surveillance System (eCBSS) tracks patients and treatment adherence for Drug-Susceptible TB (DS-TB) but lacks integration with machine learning (ML) for predictive analytics and data-driven decision-making. This study developed and tested an ML-powered web application integrated into DHIS2’s eCBSS to predict the likelihood of MDR-TB development among DS-TB patients and identify geographic hotspots. Objective: The study aimed to gather user requirements, develop, and validate an ML-integrated analytical dashboard within DHIS2’s eCBSS to support healthcare workers in predicting MDR-TB risk and visualizing hotspots through a GIS module. Methodology: A Design Science Research approach guided the development, using a User-Centered Design framework. In-depth interviews were conducted with healthcare workers from three high-volume TB treatment facilities in Uganda’s Central Region, all using eCBSS. Requirements informed the development of a one-dimensional deep neural network (1DNN) in Python and a web interface built with JavaScript and Node.js for local DHIS2 deployment. The ML model was validated using accuracy, AUC, and confusion matrices, while usability testing assessed ease of use and satisfaction. Results: Two major themes emerged: user requirements (functional and non-functional) and implementation needs. Functional needs included real-time alerts, risk scoring, and adherence tracking, while non-functional needs emphasized scalability, security, and usability. The 1DNN achieved 92% accuracy and an AUC of 0.96, demonstrating strong predictive performance. The GIS module effectively visualized MDR-TB hotspots, supporting targeted interventions. Usability testing showed high satisfaction (mean scores 4.65–4.87/5), though participants recommended improved search, filtering, and clearer error handling. Conclusion: Integrating ML and GIS into DHIS2’s eCBSS significantly enhanced predictive capacity, spatial analysis, and resource allocation, demonstrating the potential of AI-driven tools to strengthen MDR-TB management in Uganda.