Background: Chronic Kidney Disease (CKD) is a significant complication in people with diabetes, leading to serious adverse health outcomes and increased healthcare costs globally individually and on healthcare systems. This problem become more complicated when it is in Low and middle in countries including Rwanda when access to early diagnostic services is limited. Early prediction and intervention can improve patient outcomes and reduce the burden on healthcare systems. Objective: This study aimed to develop and evaluate a machine learning model for predicting CKD in diabetic patients, tailored to the Rwandan population, using Electronic Medical record Data. Methodology:  Secondary data were extracted from OpenClinic, an electronic medical record (EMR) system used at Kigali University Hospital, covering a period of 10 years from 2013 to 2023. The final cleaned dataset was used to train four machine-learning models: Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Extra Gradient Boosting Machine (XGBoost). XGboost was noted as the best performer with the AUC score of 0.98 and accuracy of 95.67%. Results: The findings revealed that XGBoost was highly effective in predicting chronic kidney disease, achieving an accuracy of 95.76% and an AUC score of 0.98. Given that the dataset was collected from the local population, this study confirms that machine learning algorithms can assist clinicians in Rwanda in diagnosing chronic kidney disease in its early stages. Conclusion:  This study demonstrates the potential of machine learning models in predicting chronic kidney disease (CKD) in diabetic patients, highlighting the importance of local datasets for optimizing model performance in specific populations. These findings suggest that machine learning can effectively assist existing medical techniques in the early diagnosis of CKD in Rwanda.

The authors have declared no competing interest.

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The author(s) received no specific funding for this work.

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The IRB of University of Rwanda, College of Medicine and Health Sciences issued ethical clearance. Additionally, the Ethical committee at University Teaching Hospital clearance to access data.

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