Article: Prediction of type 2 diabetes based on feature augmentation and Morlet wavelet assisted deep learning network with FFT overlap and add convolution Journal: International Journal of Biomedical Engineering and Technology (IJBET) 2025 Vol.47 No.3 pp.259 - 286 Abstract: The enormous development in technology makes diagnosing easier in the medical field under various existing approaches. Even though the approach possesses some disadvantages like lowering disease treatment costs, research showed that network features, which are important in decision-making but have a low accuracy value, were critical. A new deep-learning technique for diabetes detection is proposed in this research that resolves the challenges in the existing. The approach combines adversarial variational auto-encoder (AVAE) for data/feature augmentation with a Morlet wavelet-assisted deep learning network featuring fast Fourier transform (FFT) overlap and add convolution (MW-FFT-OAconv) to enhance classification accuracy. A novel optimiser, the weighted mean of vectors (WMOV), is introduced to acquire the weight parameters of the MW-FFT-OAconv network. Experimental results evaluated using statistical measures such as accuracy, F1-score, precision, true negative rate (TNR), true positive rate (TPR), classifier error percentage (CEP), and Mathew coefficient correlation (MCC), demonstrate the effectiveness of the proposed approach. Compared to previous machine learning prediction models (random forest, naive Bayes, and decision tree), the proposed technique achieves an accuracy level of 98.44% in predicting type 2 diabetes. Inderscience Publishers - linking academia, business and industry through research

Title: Prediction of type 2 diabetes based on feature augmentation and Morlet wavelet assisted deep learning network with FFT overlap and add convolution

Authors: Hitesh B. Patel; Keyur Brahmbhatt

Addresses: Gujarat Technological University, Chandkheda, Ahmedabad, Gujarat 382424, India ' IT Department, BVM Engineering College, VV Nagar, Gujarat 388120, India

Abstract: The enormous development in technology makes diagnosing easier in the medical field under various existing approaches. Even though the approach possesses some disadvantages like lowering disease treatment costs, research showed that network features, which are important in decision-making but have a low accuracy value, were critical. A new deep-learning technique for diabetes detection is proposed in this research that resolves the challenges in the existing. The approach combines adversarial variational auto-encoder (AVAE) for data/feature augmentation with a Morlet wavelet-assisted deep learning network featuring fast Fourier transform (FFT) overlap and add convolution (MW-FFT-OAconv) to enhance classification accuracy. A novel optimiser, the weighted mean of vectors (WMOV), is introduced to acquire the weight parameters of the MW-FFT-OAconv network. Experimental results evaluated using statistical measures such as accuracy, F1-score, precision, true negative rate (TNR), true positive rate (TPR), classifier error percentage (CEP), and Mathew coefficient correlation (MCC), demonstrate the effectiveness of the proposed approach. Compared to previous machine learning prediction models (random forest, naive Bayes, and decision tree), the proposed technique achieves an accuracy level of 98.44% in predicting type 2 diabetes.

Keywords: adversarial variational auto-encoder; AVAE; MW-FFT-OAconv; weighted mean of vectors; WMOV; classifier error percentage; CEP; Mathew coefficient correlation; MCC.

DOI: 10.1504/IJBET.2025.144949

International Journal of Biomedical Engineering and Technology, 2025 Vol.47 No.3, pp.259 - 286

Received: 23 Dec 2023
Accepted: 11 Apr 2024
Published online: 13 Mar 2025 *

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Title: Prediction of type 2 diabetes based on feature augmentation and Morlet wavelet assisted deep learning network with FFT overlap and add convolution

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