An Edge-Cloud-Aided Private High-Order Fuzzy C-Means Clustering Algorithm in Smart Healthcare

Smart healthcare has emerged to provide healthcare services using data analysis techniques. Especially, clustering is playing an indispensable role in analyzing healthcare records. However, large multi-modal healthcare data imposes great challenges on clustering. Specifically, it is hard for traditional approaches to obtain desirable results for healthcare data clustering since they are not able to work for multi-modal data. This paper presents a new high-order multi-modal learning approach using multimodal deep learning and the Tucker decomposition (F- HoFCM). Furthermore, we propose an edge-cloud-aided private scheme to facilitate the clustering efficiency for its embedding in edge resources. Specifically, the computationally intensive tasks, such as parameter updating with high-order back propagation algorithm and clustering through high-order fuzzy c-means, are processed in a centralized location with cloud computing. The other tasks such as multi-modal data fusion and Tucker decomposition are performed at the edge resources. Since the feature fusion and Tucker decomposition are nonlinear operations, the cloud cannot obtain the raw data, thus protecting the privacy. Experimental results state that the presented approach produces significantly more accurate results than the existing high-order fuzzy c-means (HOFCM) on multi-modal healthcare datasets and furthermore the clustering efficiency are significantly improved by the developed edge-cloud-aided private healthcare system.