Аңдатпа
This article presents an integrated approach to predicting diabetes progression based on an ensemble of multiple deep neural network architectures. To enhance diagnostic accuracy and reliability, convolutional neural networks (CNN), recurrent neural networks (RNN), and long short-term memory (LSTM) models are jointly utilized within a clinical decision support framework. The optimal combination of their predictions is achieved through the Dirichlet ensemble method, which adaptively distributes weights among individual models according to their validation performance. Hyperparameter optimization using the Grid Search algorithm allows systematic selection of training parameters, network depth, activation functions, and regularization techniques, ensuring better convergence and reducing overfitting risks. The study involves a comprehensive data preprocessing pipeline, including normalization, balancing, and One-Hot Encoding of categorical features, to manage heterogeneous medical datasets and minimize the effect of missing or noisy information. Experimental evaluation demonstrates that the proposed ensemble model significantly outperforms individual CNN, RNN, and LSTM architectures in terms of accuracy, sensitivity, and stability, achieving improved generalization ability and robustness to data variability. This research emphasizes the potential of ensemble deep learning approaches to strengthen modern clinical decision support systems (CDSS). The developed framework enables more precise and interpretable diagnostic predictions, contributing to early diabetes detection and prevention strategies. Furthermore, the proposed methodology can be extended to other medical classification problems, providing a flexible and adaptive analytical tool for healthcare applications. The findings confirm that adaptive ensemble methods based on the Dirichlet distribution can serve as a foundation for reliable, transparent, and intelligent clinical decision-making in future healthcare systems.