A novel blockchain-enabled heart disease prediction mechanism using machine learning. (July 2022)

• Record Type: Journal Article
• Authors: Hasanova, Huru; Tufail, Muhammad; Baek, Ui-Jun; Park, Jee-Tae; Kim, Myung-Sup
• Is Part Of: Computers & electrical engineering. Volume 101(2022)
• Publication Date: 2022-07
• Subjects: Blockchain -- Sine cosine algorithm -- K-Nearest Neighbour -- Weighted K-Nearest Neighbour -- Heart disease; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108086
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A framework for patient data management based on blockchain that ensures the integrity of patient records. A novel heart disease prediction algorithm based on behavioural factors of human to mitigate the disease in its earlier stage using meta heuristic algorithm. A comprehensive strategy to ensure the authenticity of the learning data through multi-tier blockchain based learning source. Abstract: Heart disease is one of the leading causes for death in men and women across the globe. Several characteristics that can be monitored to predict the heart disease in the earlier stage are blood pressure, cholesterol level, blood sugar level and body weight. The technology is revolutionizing the existing healthcare infrastructure. With the inclusion of Internet of Things (IoT), now we can monitor patients remotely, store their data, and process it for further analysis. However, the need is to propose new and advanced secured algorithms for fast processing and efficient detection of events. In this article, a machine learning based Sine Cosine Weighted K-Nearest Neighbour (SCA_WKNN) algorithm is proposed for the heart disease prediction that learns from the data being stored in blockchain. Since the data stored in the blockchain are tamper resistant, it acts as an authentic source for learning data and also as a secure storage environment for patient information. The performance of proposed SCA_WKNN is assessed in comparison with other algorithms in terms of accuracy, precision, recall, F-score, and root mean square error. Our analysis indicates that SCA_WKNN achieves 4.59% and 15.61% maximum accuracy than W K-NN and K-NN, respectively. Also, blockchain-based storage is compared with peer-to-peer storage in terms of latency and throughput. The blockchain-based decentralized storage achieves 25.03% maximum throughput than peer-to-peer storage. Graphical abstract: Image, graphical abstract