Deep Learning Ensemble for Diabetes Prediction: Integrating LSTM, DCNN, and SMOTE for Enhanced Risk Assessment

Diabetes is a prevalent and chronic medical condition that poses a significant public health challenge worldwide. Accurate early prediction of diabetes risk can facilitate timely interventions and improve patient outcomes. In this study, we propose a comprehensive approach to diabetes risk assessment through the integration of deep learning techniques and data augmentation strategies. Our research leverages the power of deep learning ensembles by combining Long Short-Term Memory (LSTM) networks, Deep Convolutional Neural Networks (DCNN), and Synthetic Minority Over-sampling Technique (SMOTE) for addressing class imbalance. We begin by collecting and preprocessing a dataset comprising patient information, medical histories, and, where applicable, image data. The LSTM model is tailored to capture temporal dependencies in the data, while the DCNN extracts meaningful features. By integrating SMOTE, we tackle class imbalance challenges commonly encountered in medical diagnosis tasks. Our ensemble model seamlessly integrates the predictions from these diverse models, allowing for a holistic assessment of diabetes risk. Extensive evaluation and hyperparameter tuning are performed to optimize performance and ensure the model’s robustness. Cross-validation techniques are employed to gauge the model’s generalization capabilities. The proposed approach aims to not only provide accurate predictions but also to offer insights into the contributions of individual models within the ensemble. Ultimately, our research seeks to enhance diabetes risk assessment, offering a valuable tool for healthcare practitioners in making informed decisions and potentially improving the early detection and management of diabetes. This present study represents a significant step towards more reliable and comprehensive diabetes risk assessment, leveraging the potential of deep learning and ensemble techniques to advance predictive accuracy and broaden our understanding of this critical healthcare challenge. An accuracy score of 74% suggests that the model performs reasonably well in making correct predictions overall, although it doesn’t achieve a very high level of accuracy. On the other hand, an ROC AUC value of 0.81 indicates that the model is effective at distinguishing between positive and negative instances, showing its ability to differentiate between the two classes.