ARTIFICIAL INTELLIGENCE FOR EARLY DETECTION OF DIABETES COMPLICATIONS
DOI:
https://doi.org/10.60787/jnamp.v67i2.374Keywords:
Artificial intelligence, Diabetes Complications, Early detection, Machine Learning, Predictive AnalysisAbstract
Diabetes mellitus is a prevalent chronic disease that often leads to severe complications such as diabetic retinopathy, nephropathy, and neuropathy. Early detection of these complications is critical for timely intervention and improved patient outcomes. This study aims to develop AI models to predict and detect early signs of diabetes complications using patient data from electronic health records (EHR) and lab results. By leveraging machine learning algorithms and deep learning techniques, we seek to identify patterns and correlations in the data that indicate the onset of complications. Our models were validated using cross-validation and tested in a real-world clinical setting to ensure robustness and applicability. The results demonstrate significant improvements in early detection rates, allowing for proactive patient management and potentially reducing the burden of diabetes-related complications on healthcare systems.
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