Integration of Machine Learning-Based Type 2 Diabetes Prediction Models Into Clinical Workflow Systems
Keywords:
clinical workflow integration, complications of diabetes mellitus, convolutional neural network, electronic health records, random forestAbstract
Complications of diabetes mellitus are the main causes of morbidity, mortality, and increased health care costs. In recent years, machine learning-based prediction models, particularly Random Forest (RF) and Convolutional Neural Network (CNN), have shown promising capabilities in identifying diabetic patients who are at risk of complications. This systematic review aims to analyze the extent of integration of RF and CNN-based diabetes complication prediction models into clinical workflow systems, evaluate the operational feasibility of their implementation, and identify key barriers to their adoption. The study was conducted following the guidelines of PRISMA 2020 through a literature search on PubMed, Scopus, and Web of Science for publication in 2015-2026. Studies that meet inclusion criteria include the use of RF or CNN to predict diabetes complications in the context of health care, electronic health records, or clinical decision support systems. Data was synthesized using a thematic narrative synthesis approach. The results of the study show that most of the RF and CNN models are still in the non-integrated or semi-integrated stage. RF is easier to implement because it has better interpretability and computational efficiency, whereas CNN shows high predictive performance especially in medical imaging-based complications, but faces challenges related to computational needs and model transparency. Factors that affect the success of the implementation include system interoperability, data quality, infrastructure readiness, and user acceptance. Further implementation research is needed to support the continued integration of RF and CNN models in clinical practice.
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