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Purpose: Machine learning models are used to develop and improve various disease prediction systems. Ensemble learning is a machine learning technique that combines many classifiers to increase performance by making more accurate predictions than a single classifier. Although several researchers have employed ensemble techniques for disease prediction, a comprehensive comparative study of these techniques still needs to be provided. Methods: Using 16 disease datasets from Kaggle and the UCI Machine Learning Repository, this study compares the performance of 15 variants of ensemble techniques for disease prediction. The comparison was performed using six performance measures: accuracy, precision, recall, F1 score, AUC (Area Under the receiver operating characteristics Curve) and AUPRC (Area Under the Precision-Recall Curve). Results: Stacking variant of Multi-level stacking showed superior disease prediction performance compared with other bagging and boosting variants, followed by another stacking variant (Classical stacking). Overall, stacking outperformed bagging and boosting for disease prediction. Logit Boost showed the worst performance. Conclusion: The findings of this study can help researchers select an appropriate ensemble approach for future studies focusing on accurate disease prediction.

In this study, we propose a modified version of the widely used UNet architecture, enhanced by the integration of recurrent blocks at each step of the encoder (down-sampling) and decoder (up-sampling) stages. The proposed Recurrent UNet (R-UNet) architecture aims to improve the performance of semantic segmentation tasks by allowing the model to capture temporal dependencies and long-range contextual information. The R-UNet architecture consists of two main components: a recurrent encoder and a recurrent decoder. The recurrent encoder is composed of a series of convolutional and recurrent blocks, which extract features from the input image and propagate them across time. The recurrent decoder consists of a similar series of convolutional and recurrent blocks, which use the extracted features to generate the final segmentation mask. An attention mechanism is employed to enhance feature extraction at the bottleneck of the model. The proposed R-UNet architecture is evaluated on multiple benchmark datasets, including those for liver segmentation, brain tumor detection, mitochondria segmentation, lung imaging, a proprietary lung CT COVID-19 dataset, as well as various multi-organ imaging datasets. The experimental results demonstrate that the proposed R-UNet architecture outperforms the standard UNet architecture and several other state-of-the-art semantic segmentation models in terms of accuracy score, achieving an overall accuracy of 97.2 % on the Mitochondria dataset, 97.83 % on the Liver dataset, 89.17 % on the Tumor dataset and 97.22 % Lung dataset.

As a subset of machine learning, meta-learning, or learning to learn, aims at improving the model’s capabilities by employing prior knowledge and experience. A meta-learning paradigm can appropriately tackle the conventional challenges of traditional learning approaches, such as insufficient number of samples, domain shifts, and generalization. These unique characteristics position meta-learning as a suitable choice for developing influential solutions in various healthcare contexts, where the available data is often insufficient, and the data collection methodologies are different. This survey discusses meta-learning broad applications in the healthcare domain to provide insight into how and where it can address critical healthcare challenges. We first describe the theoretical foundations and pivotal methods of meta-learning. We then divide the employed meta-learning approaches in the healthcare domain into two main categories of multi/single-task learning and many/few-shot learning and survey the studies. Finally, we highlight the current challenges in meta-learning research, discuss the potential solutions, and provide future perspectives on meta-learning in healthcare.

Video-based face, expression, and scene recognition are fundamental problems in human–machine interaction, especially when there is a short-length video. In this paper, we present a new derivative sparse representation approach for face and texture recognition using short-length videos. First, it builds local linear subspaces of dynamic texture segments by computing spatiotemporal directional derivatives in a cylinder neighborhood within dynamic textures. Unlike traditional methods, a nonbinary texture coding technique is proposed to extract high-order derivatives using continuous circular and cylinder regions to avoid aliasing effects. Then, these local linear subspaces of texture segments are mapped onto a Grassmann manifold via sparse representation. A new joint sparse representation algorithm is developed to establish the correspondences of subspace points on the manifold for measuring the similarity between two dynamic textures. Extensive experiments on the Honda/UCSD, the CMU motion of body, the YouTube, and the DynTex datasets show that the proposed method consistently outperforms the state-of-the-art methods in dynamic texture recognition, and achieved the encouraging highest accuracy reported to date on the challenging YouTube face dataset. The encouraging experimental results show the effectiveness of the proposed method in video-based face recognition in human–machine system applications.