An Ensemble Deep Learning Model for Diabetes Disease Prediction
Keywords:
Deep Learning, Diabetes, Ensemble Learning, Healthcare, LSTMAbstract
Diabetes remains a significant health challenge with serious consequences if left undiagnosed or untreated. Addressing the issues of accurately labeled data, outliers, small number of samples and missing information in clinical datasets is crucial for effective diabetes prediction. Despite various efforts, there is still room for improvement in the accuracy of machine and deep learning methods for early diabetes detection. In this study, we propose a novel approach that integrates three proven deep learning models—Long Short Term Memory (LSTM), Deep Neural Networks (DNN), and Convolutional Neural Networks (CNN)—using a soft voting classifier to enhance predictive performance. Additionally, we employ data fusion to effectively address the challenge of small datasets. Our model demonstrated impressive accuracy rates when evaluated on the Pima Indian Diabetes Dataset (PIDD), the Frankfurt Hospital Germany Diabetes Dataset (FHGDD), and a combined dataset: 85.9% on PIDD, 98.0% on FHGDD, and 99.81% on the combined dataset. These results outperform those of individual classifiers, highlighting the effectiveness of our method in diabetes prediction.
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