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Deep learning has seen significant growth recently and is now applied to a wide range of conventional use cases, including graphs. Graph data provides relational information between elements and is a standard data format for various machine learning and deep learning tasks. Models that can learn from such inputs are essential for working with graph data effectively. This paper identifies nodes and edges within specific applications, such as text, entities, and relations, to create graph structures. Different applications may require various graph neural network (GNN) models. GNNs facilitate the exchange of information between nodes in a graph, enabling them to understand dependencies within the nodes and edges. The paper delves into specific GNN models like graph convolution networks (GCNs), GraphSAGE, and graph attention networks (GATs), which are widely used in various applications today. It also discusses the message-passing mechanism employed by GNN models and examines the strengths and limitations of these models in different domains. Furthermore, the paper explores the diverse applications of GNNs, the datasets commonly used with them, and the Python libraries that support GNN models. It offers an extensive overview of the landscape of GNN research and its practical implementations.

In today's world, mental health conditions including stress, worry, and depression are more common, particularly among working adults and students. To stop these problems from getting worse, prompt detection and treatment are crucial. This study examines how emotional and behavioural indicators might be used to predict mental health issues using machine learning (ML) algorithms. A mental health dataset was used to train and assess a number of machines learning models, including Logistic Regression, K-Nearest Neighbours, Decision Tree, Random Forest, Gradient Boosting, XGBoost, and a Hybrid ensemble model. Their efficacy was evaluated using performance criteria such F1-score, recall, accuracy, and precision. The Hybrid ensemble approach outperformed conventional algorithms and had the greatest accuracy of 85% among the models that were assessed. The findings show that ensemble approaches have better prediction capacities for mental health detection, especially hybrid strategies. The potential of machine learning to enhance early diagnosis and individualized mental health support systems is highlighted by this study.

Sentiment analysis using graph neural networks (GNN) is an innovative idea to revolutionize sentiment analysis in the complex healthcare landscape. It aims to extract and contextualize sentiments from diverse healthcare data sources, including patient reviews, medical notes, and social media content. The system, with key components such as data pre-processing, feature extraction, and GNN model development, creates a graph-based representation to capture intricate relationships between patients, medical providers, treatments, and sentiments. This empowers healthcare professionals to make data-driven decisions for improving patient satisfaction, service quality, and operational efficiency. The system can also monitor public health sentiment trends, identify potential issues in healthcare services and patient well-being. Ultimately, it aligns with the vision of a more patient-centric, data-driven, and empathetic healthcare ecosystem, offering actionable insights for healthcare professionals, researchers, and policymakers. This research signifies the fusion of advanced technology, healthcare innovation, and data-driven decision-making, reshaping the healthcare sector towards a more effective and patient-focused system. In this project, we tested the accuracy of the model using various machine learning (ML) algorithms like support vector machine (SVM), naïve bayes, random forest (RF), and logistic regression (LR). In this project, we used ML algorithms such as SVM, nave bayes, random forest, and logistic regression to test the model’s accuracy. We then tested it using GNN, and GNN provided the highest accuracy of any model, so we used it as our final model.