Integrating Blockchain and Explainable Neural Networks for Secure Healthcare Data Systems

In order to make DL models better, feature selection gets rid of features that aren't needed. The proposed approach improves accuracy and computing speed by filtering features. When applied to estimation and learning, the XNN yields better results. The dataset is prepared for training by the model using filter-based feature selection. This method sorts features according to their statistical importance and then picks the most important ones to make learning as efficient as possible. Applying adaptive learning to the optimised dataset, the XNN architecture processes it and increases classification accuracy. The computational efficiency of the model is compared to that of conventional approaches. As far as efficiency and accuracy are concerned, the suggested XNN model is head and shoulders above the competition. With a maximum accuracy of 99.28%, the model clearly knows how to estimate and choose attributes correctly. This data-driven approach to decision-making enhances accuracy and efficiency through the use of XNN and filter-based feature selection. In terms of high-performance predictive modelling, the results show that XNN is a viable option. Research in the future can look at how well it scales up or down depending on the dataset and the use case.