Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Abstract

Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world's diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals' lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model's poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6.

Enhanced Colon Cancer Segmentation and Image Synthesis Through Advanced Generative Adversarial Networks Based-Sine Cosine Algorithm

“…For instance, in oncology, accurate segmentation of tumors allows for better assessment of their size, shape, and location, guiding radiation therapy and surgical interventions [13]. Furthermore, the advancements in deep learning and artificial intelligence have revolutionized the segmentation of medical images, with U-Net architectures [14], Convolutional Neural Networks (CNNs) [15], [16], You Only Look Once (YOLO) [17], [18], and Generative Adversarial Networks (GANs) [19] demonstrating exceptional performance in segmenting complex anatomical structures. Such technologies contribute significantly to improving patient outcomes by facilitating more personalized and targeted medical interventions [20], [21].…”