Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have employed ensemble approaches for disease prediction, there is a lack of thorough assessment of commonly used ensemble approaches against highly researched diseases. Consequently, this study aims to identify significant trends in the performance accuracies of ensemble techniques (i.e., bagging, boosting, stacking, and voting) against five hugely researched diseases (i.e., diabetes, skin disease, kidney disease, liver disease, and heart conditions). Using a well-defined search strategy, we first identified 45 articles from the current literature that applied two or more of the four ensemble approaches to any of these five diseases and were published in 2016–2023. Although stacking has been used the fewest number of times (23) compared with bagging (41) and boosting (37), it showed the most accurate performance the most times (19 out of 23). The voting approach is the second-best ensemble approach, as revealed in this review. Stacking always revealed the most accurate performance in the reviewed articles for skin disease and diabetes. Bagging demonstrated the best performance for kidney disease (five out of six times) and boosting for liver and diabetes (four out of six times). The results show that stacking has demonstrated greater accuracy in disease prediction than the other three candidate algorithms. Our study also demonstrates variability in the perceived performance of different ensemble approaches against frequently used disease datasets. The findings of this work will assist researchers in better understanding current trends and hotspots in disease prediction models that employ ensemble learning, as well as in determining a more suitable ensemble model for predictive disease analytics. This article also discusses variability in the perceived performance of different ensemble approaches against frequently used disease datasets.

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.

Hospital readmission and length-of-stay predictions provide information on how to manage hospital bed capacity and the number of required staff, especially during pandemics. We present a hybrid deep model called the Genetic Algorithm-Optimized Convolutional Neural Network (GAOCNN), with a unique preprocessing method to predict hospital readmission and the length of stay required for patients of various conditions. GAOCNN uses one-dimensional convolutional layers to predict hospital readmission and the length of stay. The parameters of the layers are optimized via a genetic algorithm. To show the performance of the proposed model in patients with various conditions, we evaluate the model under three healthcare datasets: the Diabetes 130-US hospitals dataset, the COVID-19 dataset, and the MIMIC-III dataset. The diabetes 130-US hospitals dataset has information on both readmission and the length of stay, while the COVID-19 and MIMIC-III datasets just include information on the length of stay. Experimental results show that the proposed model’s accuracy for hospital readmission was 97.2% for diabetic patients. Furthermore, the accuracy of the length-of-stay prediction was 89%, 99.4%, and 94.1% for the diabetic, COVID-19, and ICU patients, respectively. These results confirm the superiority of the proposed model compared to existing methods. Our findings offer a platform for managing the healthcare funds and resources for patients with various diseases.

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.