Implementation of Big Data Analytics for Diabetes Management: Needs Assessment in the Rwanda Healthcare System
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arXiv
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| Format: | Preprint |
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2026
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| author | Majyambere, Silas Lindgren, Tony Ayele, Workneh Y. Twizere, Celestin |
| author_facet | Majyambere, Silas Lindgren, Tony Ayele, Workneh Y. Twizere, Celestin |
| contents | Diabetes is a chronic metabolic disease that can lead to serious health problems if not diagnosed and managed early. Big Data Analytics (BDA) and machine learning offer practical tools for analyzing large health datasets and supporting early detection and better treatment decisions. However, their use in routine clinical practice is still limited. This study examines the readiness of Rwanda's healthcare system to adopt big data analytics for diabetes management. As the country continues to expand its use of electronic medical records and health information systems, new opportunities arise for improving prediction, monitoring, and clinical decision-making. A five-day workshop involving 25 key stakeholders, including clinicians, data managers, policymakers, medical researchers, nutritionists, and technology providers, was conducted to assess preparedness and identify existing gaps. The findings highlight both the potential and the main challenges of BDA implementation. Based on these results, the paper proposes a practical BDA framework to support diabetes management strategies using explainable machine learning models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_26786 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Implementation of Big Data Analytics for Diabetes Management: Needs Assessment in the Rwanda Healthcare System Majyambere, Silas Lindgren, Tony Ayele, Workneh Y. Twizere, Celestin Computers and Society Artificial Intelligence Machine Learning Diabetes is a chronic metabolic disease that can lead to serious health problems if not diagnosed and managed early. Big Data Analytics (BDA) and machine learning offer practical tools for analyzing large health datasets and supporting early detection and better treatment decisions. However, their use in routine clinical practice is still limited. This study examines the readiness of Rwanda's healthcare system to adopt big data analytics for diabetes management. As the country continues to expand its use of electronic medical records and health information systems, new opportunities arise for improving prediction, monitoring, and clinical decision-making. A five-day workshop involving 25 key stakeholders, including clinicians, data managers, policymakers, medical researchers, nutritionists, and technology providers, was conducted to assess preparedness and identify existing gaps. The findings highlight both the potential and the main challenges of BDA implementation. Based on these results, the paper proposes a practical BDA framework to support diabetes management strategies using explainable machine learning models. |
| title | Implementation of Big Data Analytics for Diabetes Management: Needs Assessment in the Rwanda Healthcare System |
| topic | Computers and Society Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2605.26786 |