Cloud Computing with Artificial Intelligence Techniques: Hybrid FA-CNN and DE-ELM Approaches for Enhanced Disease Detection in Healthcare Systems

Abstract

Background: Disease detection has grown increasingly effective with the quick development of artificial intelligence (AI) and cloud computing (CC), particularly through the real-time processing of large amounts of intricate medical data from Internet of Things (IoT) devices. Accurate and fast disease diagnosis is limited by traditional approaches' difficulties with handling high-dimensional data.
Objective: Utilizing the advantages of fuzzy logic and evolutionary optimization, this study attempts establishing a hybrid model that combines the Fuzzy Aggregation Convolutional Neural Network (FA-CNN) and Differential Evolutionary-Extreme Learning Machine (DE-
ELM) to improve disease detection accuracy, sensitivity, and computational efficiency in healthcare. Methods: In order to maximize classification accuracy, the suggested model combines DE- ELM with FA-CNN for processing ambiguous healthcare data. The system is more resilient to noisy IoT data if data preprocessing is used, such as feature extraction and normalization. Analyzed and contrasted with conventional techniques are performance parameters such computation time, sensitivity, specificity, and accuracy. Results: FA-CNN + DE-ELM outperformed current models by achieving superior outcomes with a computation time of 65 seconds, accuracy of 95%, sensitivity of 98%, and specificity of 95%. High efficacy in early disease identification and real-time healthcare monitoring is demonstrated by this hybrid technique. Conclusion: A reliable approach to disease identification that maximizes data processing and
diagnostic precision is provided by the FA-CNN + DE-ELM hybrid model. The model is positioned as a viable tool for proactive, real-time healthcare diagnostics by combining fuzzy logic with evolutionary algorithms, that improves handling of inaccurate medical data.