Cloud Computing with Artificial Intelligence Techniques: BBO-FLC and ABC-ANFIS Integration for Advanced Healthcare Prediction Models

Abstract

Background: Cloud computing (CC) and artificial intelligence (AI) are causing a rapid evolution in healthcare, meeting the requirement for accurate and effective disease diagnosis and management through wearable IoT devices and sophisticated algorithms.
Objective: To develop a BBO-FLC and ABC-ANFIS system that works together for better disease prediction accuracy and real-time monitoring.
Methods: Implemented on a scalable cloud architecture, the system combines IoT-enabled sensors for data gathering, ABC for feature optimization, BBO for fuzzy rule refining, and ANFIS for disease categorization.
Results: The suggested solution outperformed conventional techniques with 96% accuracy, 98% sensitivity, and 95% specificity at a 60-second computation time reduction.
Conclusion: The precision, scalability, and real-time healthcare applications for complicated disease prediction and monitoring could be greatly improved by this integrated system.