An Enhanced Artificial Neural Network Mode for Type 2 Diabetes Classification Using SMOTE and SMOTE-Tomek with Effective Feature Selection Methods

E. Sabitha; M. Durgadevi

doi:10.1007/978-3-031-73068-9_8

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An Enhanced Artificial Neural Network Mode for Type 2 Diabetes Classification Using SMOTE and SMOTE-Tomek with Effective Feature Selection Methods

E. Sabitha M. Durgadevi

Year: 2024 Communications in computer and information science Pages: 84-104 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-031-73068-9_8

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Keywords:

Computer science Artificial neural network Feature selection Artificial intelligence Selection (genetic algorithm) Mode (computer interface) Pattern recognition (psychology) Feature (linguistics) Operating system

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Artificial Intelligence in Healthcare

Health Sciences → Health Professions → Health Information Management

Imbalanced Data Classification Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Machine Learning in Healthcare

Physical Sciences → Computer Science → Artificial Intelligence

An Enhanced Artificial Neural Network Mode for Type 2 Diabetes Classification Using SMOTE and SMOTE-Tomek with Effective Feature Selection Methods

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