Tehreem Fatima1, Kewen Xia1,*, Wenbiao Yang2, Qurat Ul Ain1, Poornima Lankani Perera1
CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 811-826, 2025, DOI:10.32604/cmc.2025.063686
- 09 June 2025
Abstract The rising prevalence of diabetes in modern society underscores the urgent need for precise and efficient diagnostic tools to support early intervention and treatment. However, the inherent limitations of existing datasets, including significant class imbalances and inadequate sample diversity, pose challenges to the accurate prediction and classification of diabetes. Addressing these issues, this study proposes an innovative diabetes prediction framework that integrates a hybrid Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) model for classification with Adaptive Synthetic Sampling (ADASYN) for data augmentation. ADASYN was employed to generate synthetic yet representative data samples, effectively mitigating class… More >
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