<![CDATA[Diabetes is a global health crisis, and early prediction is critical to preventing serious complications. Recent research shows that ensemble machine learning methods and deep learning architectures significantly improve diabetes prediction accuracy. Ensemble methods such as random forest, XGBoost, bagging, boosting, and stacking utilize multiple algorithms to capture diverse data patterns and consistently outperform traditional single classifiers. In parallel, deep learning models, such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and hybrid CNN-LSTM architectures, excel at identifying complex temporal and spatial relationships. These techniques are widely applied to benchmark datasets, such as the Pima Indian diabetes data and other repositories at the University of California, Irvine, and are evaluated through metrics including area under the curve (AUC-ROC), precision, and recall. Challenges remain—particularly computational cost and model interpretabilitybut both approaches deliver superior accuracy and reliability. By integrating current evidence, this overview highlights the potential of ensemble learning and deep learning methods to enable earlier and more accurate detection of diabetes and enhance personalized healthcare solutions.]]>
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