Innovative Hybrid Models for Predicting Diabetes: CNN-LSTM Hybrid and Calibrated Soft Voting Model
AUC-ROC Curves; CNN; Confusion Matrix; Deep Learning; Diabetes Prediction; F1 Score; Hard Voting; LSTM; Soft Voting; Stacking
This study assesses four ensemble techniques - stacking, soft voting, hard voting, and calibrated soft voting - for predicting diabetes onset using the Pima Indians Diabetes dataset. Traditional single-model methods are contrasted with these advanced ensemble approaches, which integrate multiple models to enhance predictive accuracy. The evaluation included metrics such as accuracy, precision, recall, F1 score, and AUC. The CNN-LSTM model was also examined, achieving an accuracy of 75%, precision of 70%, recall of 69%, and an F1 score of 72%. Among the suggested methods, the calibrated soft vote model was the most effective, with improved performance compared to the rest of the techniques. Upcoming studies will address the combination of these models with real-time monitoring systems and deploying their use across a broad range of datasets and medical conditions. 2025 IEEE.
Proceedings of 5th International Conference on Soft Computing for Security Applications, ICSCSA 2025;pp.2078-2083
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICSCSA66339.2025.11170763 https://www.scopus.com/pages/publications/105018453839?origin=resultslist
George J., Christ (Deemed to Be University), India; George J., Christ (Deemed to Be University), India