Climate Change Impact on Geographical Region and Healthcare Analysis Using Deep Learning Algorithms

Climate change is a worldwide concern that needs to be taken into account and handled right away. On adaptation and mitigation of climate change, numerous articles are published. To investigate intricacies of climate change as well as develop more effective and economical policies for reduction and adaptation, new approaches are necessary. Climate change is one of the many domains where machine learning (ML) and deep learning (DL) methods have become increasingly prominent as a result of technological advancements. This research proposes a novel method in climate change impact in geographical region analysis and its healthcare training using deep learning model. Here, the input is gathered as a climatic analysis based on geographical change, and it is then processed for noise reduction, normalization, and smoothing. Processed data features are extracted and classified using region mask Gaussian component modeling with adversarial convolutional Boltzmann neural networks. The attributes that were retrieved display a climate change-based analysis of healthcare data. Experimental analysis is carried out in terms of training accuracy, specificity, recall, F-measure, and ROC. The proposed approach yielded 97% training accuracy, 93% recall, 90% ROC, F-measure of 92%, and 95% specificity.