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.

翻译：糖尿病是一种慢性代谢疾病，若未及早诊断和管理，可能导致严重健康问题。大数据分析与机器学习为分析大规模健康数据集、支持早期发现及优化治疗决策提供了实用工具。然而，其在常规临床实践中的应用仍然有限。本研究考察了卢旺达医疗体系采用大数据分析进行糖尿病管理的准备情况。随着该国不断扩大电子病历与健康信息系统的应用范围，在改善预测、监测及临床决策方面涌现出新的机遇。研究通过为期五天的研讨会，邀请了包括临床医生、数据管理员、政策制定者、医学研究者、营养学家及技术提供者在内的25位关键利益相关者参与，以评估准备状况并识别现有差距。研究结果揭示了大数据分析实施的潜力与主要挑战。基于上述结论，本文提出了一项实用的大数据分析框架，旨在通过可解释的机器学习模型支持糖尿病管理策略。