Efficient feature selection with attention based deep cat convolutional stacked sparse autoencoder for diabetes prediction

Key Points

Diabetes classification accuracy reached up to 98.4% across utilized datasets, indicating strong predictive power.
The method applied an improved Cheetah Optimization technique for effective feature selection in the data preprocessing phase.
Analysis included a dual attention-based deep cat convolutional stacked sparse autoencoder model to achieve superior results.
This novel approach supports early diabetes detection, suggesting further exploration could improve health outcomes.

Abstract

Diabetes, one of the most serious diseases in the world, however, early detection can prevent diabetes. This work proposes a novel approach to identifying early signs of diabetes based on deep learning methods. First, the input data is pre-processed and the features are selected using an improved Cheetah Optimization (ICO). Finally, diabetes is classified using a dual attention-based deep cat convolutional stacked sparse autoencoder model (DADCCSSAE). The proposed study improves the results and proves that the proposed method produces better results in terms of accuracy (98. 4% - dataset-1, 98% - dataset-2, 97. 4% - dataset-3, and 96. 8% - dataset-4.

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Efficient feature selection with attention based deep cat convolutional stacked sparse autoencoder for diabetes prediction

Key Points

Abstract

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