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 Nature Portfolio
Schlagworte:	639/705 692/1807 692/4019 692/499 692/699 692/700 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
Online-Zugang:	Volltext
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