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An improved Kalman filter using ANN-based learning module to predict transaction throughput of blockchain network in clinical trials

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Abstract

Clinical trials have been made transparent and accessible because of the widespread adoption of blockchain technology. Its distinctive characteristics, such as data immutability and transparency, could increase public trust in a fair and transparent manner among all stakeholders. However, blockchain systems cannot handle the requirement of processing huge volumes of data in real time. Scalability becomes a severe issue when implementing decentralized applications for clinical studies. With an abrupt expansion in the number of transaction exchanges happening consistently and the capital associated with those exchanges, there is an urgent demand for developers and users to know blockchain systems’ performance limits to determine if requirements can be fulfilled; however, little is known about the prediction of blockchain system behaviors. This paper shows the feasibility of using machine learning technologies to predict the transaction throughput of blockchain-based systems in clinical trials. A learning to prediction model is proposed, in which the Kalman filter is used to predict the transaction throughput, and the Artificial Neural Network (ANN) is utilized to enhance the Kalman filter's prediction accuracy. A real dataset generated from a clinical trial testbed using Hyperledger Fabric is utilized to demonstrate the feasibility of the proposed approach. Moreover, we compare the Kalman filter with other learning modules, and the results indicate that the ANN performs best. Furthermore, we apply the proposed approach to different blockchain platforms, and the experiment results indicate the efficiency and universality of the designed approach.

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Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

This research was supported by Shanghai Chenguang Plan (under grant number 21CGB08).

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Authors and Affiliations

  1. Business School, Shanghai Normal University Tianhua College, Shanghai, China

    Lei Hang

  2. Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan

    Israr Ullah

  3. Department of Otorhinolaryngology-Head&Neck Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Jun Yang & Chun Chen

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  1. Lei Hang
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  2. Israr Ullah
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  3. Jun Yang
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  4. Chun Chen
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Contributions

Conceptualization, C.C. and L.H.; Data analysis, L.H. and C.C.; writing—original draft preparation, C.C.; Methodology, L.H. and C.C. and I.U.; writing—review and editing, I.U. and C.C.; supervision, C.C. and J.Y. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Chun Chen.

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Hang, L., Ullah, I., Yang, J. et al. An improved Kalman filter using ANN-based learning module to predict transaction throughput of blockchain network in clinical trials. Peer-to-Peer Netw. Appl. 16, 520–537 (2023). https://doi.org/10.1007/s12083-022-01422-4

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  • DOI: https://doi.org/10.1007/s12083-022-01422-4

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