Diabetes mellitus is characterized as a chronic disease that may cause many complications. Machine learning algorithms are used to diagnose and predict diabetes. The learning-based algorithms play a vital role in supporting decision-making in disease diagnosis and prediction. In this paper, traditional classification algorithms and neural network-based machine learning are investigated for the diabetes dataset. Also, various performance methods with different aspects are evaluated for the K-nearest neighbor, Naive Bayes, extra trees, decision trees, radial basis function, and multilayer perceptron algorithms. It supports the estimation of patients who possibly suffer from diabetes in the future. This work shows that the multilayer perceptron algorithm gives the highest prediction accuracy with the lowest MSE of 0.19. The MLP gives the lowest false positive rate and false-negative rate with the highest area under curve of 86 %.

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This paper compares the performance of various popular convolutional neural network (CNN) architectures for image classification on the CIFAR10 dataset. The comparison includes CNN architectures such as Inception V3, Inception-ResNet-v2, ResNetV1, and V2, ResNeXt, MobileNet, and DenseNet, with the addition of two attention mechanisms - Convolutional Block Attention Module (CBAM), and Squeeze and Excitation (SE). CBAM and SE are believed to improve CNNs' performance, especially for complex images with multiple objects and backgrounds. The models are evaluated using loss and accuracy. The main focus of this study is to identify the most effective CNN architecture for image classification on the CIFAR10 dataset with attention mechanisms. The study aims to compare the accuracy of various CNN architectures with and without attention mechanisms and to identify the critical differences between these architectures in terms of their ability to handle complex images. The findings of this study could have implications for developing advanced CNN architectures that can potentially improve the accuracy of computer vision systems in various applications.