Blockchain-Based Secure Healthcare Application for Diabetic-Cardio Disease Prediction in Fog Computing

【Author】 Shynu, P. G.; Menon, Varun G.; Kumar, R. Lakshmana; Kadry, Seifedine; Nam, Yunyoung

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】Fog computing is a modern computing model which offers geographically dispersed end-users with the latency-aware and highly scalable services. It is comparatively safer than cloud computing, due to information being rapidly stored and evaluated closer to data sources on local fog nodes. The advent of Blockchain (BC) technology has become a remarkable, most revolutionary, and growing development in recent years. BT's open platform stresses data protection and anonymity. It also guarantees data is protected and valid through the consensus process. BC is mainly used in money-related exchanges; now it will be used in many domains, including healthcare; This paper proposes efficient Blockchain-based secure healthcare services for disease prediction in fog computing. Diabetes and cardio diseases are considered for prediction. Initially, the patient health information is collected from Fog Nodes and stored on a Blockchain. The novel rule-based clustering algorithm is initially applied to cluster the patient health records. Finally, diabetic and cardio diseases are predicted using feature selection based adaptive neuro-fuzzy inference system (FS-ANFIS). To evaluate the performance of the proposed work, an extensive experiment and analysis were conducted on data from the real world healthcare. Purity and NMI metrics are used to analyze the performance of the rule based clustering and the accuracy is used for prediction performance. The experimental results show that the proposed work efficiently predicts the disease. The proposed work reaches more than 81% of prediction accuracy compared to the other neural network algorithms.

【Keywords】Medical services; Blockchain; Edge computing; Cloud computing; Diseases; Feature extraction; Diabetes; Fog computing; blockchain; clustering; classification; fuzzy; disease prediction

【发表时间】2021

【收录时间】2022-01-02

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