A deep learning framework with hybrid stacked sparse autoencoder for type 2 diabetes prediction

Sparse numerical datasets are dominant in fields such as applied mathematics, astronomy, finance, and healthcare, presenting challenges due to their high dimensionality and sparse distribution. The predominance of zero values complicates optimal feature selection, making data analysis and model perf...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 36678 - 22
Hauptverfasser: Abdussamad, Daud, Hanita, Sokkalingam, Rajalingam, Zubair, Muhammad, Khan, Iliyas Karim, Mahmood, Zafar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 21.10.2025
Nature Publishing Group
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Schlagworte:
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Accuracy
Adaptation
Algorithms
Artificial intelligence
Astronomy
Autoencoder
Bayes Theorem
Classification
Data processing
Datasets
Deep Learning
Diabetes mellitus (non-insulin dependent)
Diabetes Mellitus, Type 2 - diagnosis
Diabetes prediction
Disease
Electronic health records
Feature selection
Genomics
Gestational diabetes
Health care
Humanities and Social Sciences
Humans
Insulin
Long short-term memory
Machine learning
Mathematical models
multidisciplinary
Natural language processing
Neural networks
Neural Networks, Computer
Predictions
Recommender systems
Regularization methods
Science
Science (multidisciplinary)
Sparse data
Sparsity
Deep learning
Sparse data
Feature selection
Diabetes prediction
Autoencoder
Machine learning
ISSN:2045-2322, 2045-2322
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Zusammenfassung:Sparse numerical datasets are dominant in fields such as applied mathematics, astronomy, finance, and healthcare, presenting challenges due to their high dimensionality and sparse distribution. The predominance of zero values complicates optimal feature selection, making data analysis and model performance more complex. To overcome this challenge, this study introduces a deep learning-based algorithm, Hybrid Stacked Sparse Autoencoder (HSSAE), which integrates and regularization with binary cross-entropy loss to improve feature selection efficiency, where regularization penalizes large weights, simplifying data representations, while regularization prevents overfitting by limiting the total weight size. Additionally, the dropout technique enhances the algorithm’s performance by randomly deactivating neurons during training, avoiding over-reliance on specific features. Meanwhile, batch normalization stabilizes weight distributions, reducing computational complexity and accelerating the convergence. The proposed algorithm, HSSAE, was evaluated against traditional classifiers, including Decision Tree, Random Forest, K-Nearest Neighbors, and Naïve Bayes, as well as deep learning-based models, such as Convolutional Neural Network, Long Short-Term Memory, and Stacked Sparse Autoencoder, in terms of Precision, Recall, Accuracy, F1-score, AUC, and Hamming Loss. Quantitatively, the proposed algorithm, HSSAE, was tested on two different sparse datasets, demonstrating superior performance with the highest accuracy of 89% on the health indicator dataset and 93% on the EHRs diabetes prediction dataset, respectively, and outperforming competing classifiers. The proposed algorithm, HSSAE, extracts features effectively and enhances robustness, making it well-suited for sparse data applications, particularly in healthcare, where high prediction accuracy is crucial.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-20534-4