Diabetes Prediction using Deep Ensemble Network

Timely diabetes detection is crucial for effective management and minimizing complications. This research presents a novel framework for early diabetes diagnosis, combining DeepEnsembleNet's capabilities with a hybrid optimization strategy. The methodology begins with robust data preprocessing, addressing missing values and outliers through advanced statistical techniques. A comprehensive feature extraction process uncoveres key insights, leveraging statistical measures and correlation analysis. Feature selection is optimized using Hybrid Immunity League Championship Algorithm (HILCA), a tailored approach integrating bio-inspired optimization techniques. The DeepEnsembleNet architecture seamlessly integrates multiple neural network types to accommodate diverse data modalities. Extensive experimentation and hyperparameter tuning have yielded promising results, demonstrating the model's potential for accurate early diabetes detection. By concurrently optimizing feature selection and hyperparameters, this hybrid approach enhances overall performance. This innovation holds significant promise for improving diabetes treatment outcomes and enhancing patient quality of life.