Synthetic Data Generation and Evaluation Techniques for Classifiers in Data Starved Medical Applications

With their ability to find solutions among complex relationships of variables, machine learning (ML) techniques are becoming more applicable to various fields, including health risk prediction. However, prediction models are sensitive to the size and distribution of the data they are trained on. ML...

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Veröffentlicht in:	IEEE access Jg. 13; S. 16584 - 16602
Hauptverfasser:	Bae, Wan D., Alkobaisi, Shayma, Horak, Matthew, Bankar, Siddheshwari, Bhuvaji, Sartaj, Kim, Sungroul, Park, Choon-Sik
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Asthma Autoencoders Boosting class imbalance problem Classification algorithms Complex variables control coefficient Data models data starved contexts Datasets Generative adversarial networks Health risk assessment Machine learning Medical services Prediction models Predictive models rare event prediction Synthetic data synthetic minority oversampling technique Training data transfer learning
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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