A Deep Learning-Based Ensemble Method for Early Detection of Diabetes

Early detection of diabetes allows healthcare professionals to treat patients at risk and improve their long-term results by implementing medical treatments and lifestyle changes. Research in the early detection of diabetes has made substantial use of machine learning techniques, especially predictive modeling algorithms. Despite the efficacy of these models, the traditional machine learning model's drawback lies in its reliance on manually created features. Deep learning models, however, exhibit promise in overcoming these shortcomings by automatically extracting complex patterns from data, resulting in more accurate and reliable early identification of diabetes. This study uses a large diabetes-related dataset and sophisticated data mining tools for predictive analysis. Examination of several machine learning and deep learning models, including K-Nearest Neighbors, Random Forest, Decision Tree, Convolutional Neural Network, Recurrent Neural Network, Long Short-Term Memory, and Gated Recurrent Unit, is utilized, and their effectiveness is evaluated. Notably, the research implements deep learning-based ensemble methods, with particular attention to two different strategies: Voting and Stacking. The ensemble approaches under study merge predictions from several models to improve the overall prediction performance. A comprehensive analysis of diverse neural network architectures reveals that deep learning-based ensemble methods improve diabetes prediction.