Integrating IoT and AI in Healthcare: A Novel MS-GNN Framework for Disease Diagnosis

Integrating the Internet of Things (IoT) and Artificial Intelligence (AI) results in smart healthcare systems. The IoT can assemble vast real-time data, while the AI suggests insights for decision-making after processing and analyzing the gathered data. The convergence of IoT with AI presents numerous potential applications in healthcare. This research introduces a novel framework for disease diagnosis in intelligent healthcare systems called Monkey Search Optimized Graph Neural Networks (MS-GNN). Initial datasets of heart disease, diabetes, and thyroid disease datasets are gathered from Kaggle. The Monkey Search algorithm, inspired by the hunting behavior of monkeys, generates solutions and systematically climbs a hierarchical structure, optimizing the objective function. The proposed hybrid model, MS-GNN, exhibits enhanced diagnosis performance compared to existing methods in terms of “accuracy, sensitivity, specificity, and AUC (Area under the Curve)” for heart disease, diabetes, and thyroid datasets. Based on experimental results, the proposed approach attained 95.87, 95.36, and 90.45% accuracy on three different datasets. The results highlight the potential of AI and IoT in smart healthcare systems, providing reliable diagnosis. The model's efficacy across different diseases demonstrates its versatility and resilience, suggesting potential achievements in intelligent healthcare.