TY - JOUR
T1 - A Novel Blockchain-Based Integrity and Reliable Veterinary Clinic Information Management System Using Predictive Analytics for Provisioning of Quality Health Services
AU - Iqbal, Naeem
AU - Jamil, Faisal
AU - Ahmad, Shabir
AU - Kim, Do Hyeun
N1 - Funding Information:
This research was supported by Energy Cloud R&D Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT (2019M3F2A1073387), and this research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2018R1D1A1A09082919), and this research was supported by Institute for Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No.2018-0-01456, AutoMaTa: Autonomous Management framework based on artificial intelligent Technology for adaptive and disposable IoT). Any correspondence related to this paper should be addressed to Dohyeun Kim.
Publisher Copyright:
© 2013 IEEE.
PY - 2021/1/14
Y1 - 2021/1/14
N2 - The recent advances in information management systems coupled with machine learning algorithms paved the way for a significant revolution in animal healthcare industries. However, the data in such systems suffer from various challenges such as security, reliability, and convenience, to name a few. Traditional systems are not useful to meet these critical issues because these systems have not a consistent structure for data security and reliability policies. Therefore, a new solution is required to enhance data accessibility and should regulate government security policies to ensure the accountability of the usage of the medical records system. Moreover, it is also required to analyze historical data of veterinary clinic using data mining and machine learning techniques to predict the future appointments scheduling requests, which is essential for veterinary management to drive better future decisions, for instance, future demands of medical supplies and to plan veterinary medical staff, etc. This paper aims to fill the gap by proposing a novel blockchain-based reliable and intelligent veterinary information management system (RIVIMS) using smart contract and machine learning techniques. The proposed RIVIMS consists of two main modules; blockchain-based secured veterinary information management, data and predictive analytics modules. First, a blockchain-based secure and reliable veterinary clinic information management system is developed using Hyperledger Fabric. Second, a smart contract enabled data, and predictive analytics modules are developed using permissioned blockchain framework. The data and predictive modules aim to analyze veterinary clinic patients appointments data in order to discover underlying patterns and build a robust prediction model using machine learning algorithms. The data and predictive helps veterinary management to drive better future business decisions to provide better healthcare services to veterinary patients. Hyperledger Caliper is used as a benchmark tool to evaluate the performance of the developed blockchain-based system in terms of transaction per second, transaction success rate, transaction throughput, and transaction latency. Furthermore, machine learning performance measures have utilized, such as MAE, RMSE, and R2 score to evaluate the overall performance of the prediction model. The experimental results demonstrate the effectiveness and robustness of the proposed RIVIMS.
AB - The recent advances in information management systems coupled with machine learning algorithms paved the way for a significant revolution in animal healthcare industries. However, the data in such systems suffer from various challenges such as security, reliability, and convenience, to name a few. Traditional systems are not useful to meet these critical issues because these systems have not a consistent structure for data security and reliability policies. Therefore, a new solution is required to enhance data accessibility and should regulate government security policies to ensure the accountability of the usage of the medical records system. Moreover, it is also required to analyze historical data of veterinary clinic using data mining and machine learning techniques to predict the future appointments scheduling requests, which is essential for veterinary management to drive better future decisions, for instance, future demands of medical supplies and to plan veterinary medical staff, etc. This paper aims to fill the gap by proposing a novel blockchain-based reliable and intelligent veterinary information management system (RIVIMS) using smart contract and machine learning techniques. The proposed RIVIMS consists of two main modules; blockchain-based secured veterinary information management, data and predictive analytics modules. First, a blockchain-based secure and reliable veterinary clinic information management system is developed using Hyperledger Fabric. Second, a smart contract enabled data, and predictive analytics modules are developed using permissioned blockchain framework. The data and predictive modules aim to analyze veterinary clinic patients appointments data in order to discover underlying patterns and build a robust prediction model using machine learning algorithms. The data and predictive helps veterinary management to drive better future business decisions to provide better healthcare services to veterinary patients. Hyperledger Caliper is used as a benchmark tool to evaluate the performance of the developed blockchain-based system in terms of transaction per second, transaction success rate, transaction throughput, and transaction latency. Furthermore, machine learning performance measures have utilized, such as MAE, RMSE, and R2 score to evaluate the overall performance of the prediction model. The experimental results demonstrate the effectiveness and robustness of the proposed RIVIMS.
KW - blockchain technology
KW - Information management system
KW - machine learning
KW - predictive analysis
KW - smart contract
UR - http://www.scopus.com/inward/record.url?scp=85099183987&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3049325
DO - 10.1109/ACCESS.2021.3049325
M3 - Article
AN - SCOPUS:85099183987
VL - 9
SP - 8069
EP - 8098
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 9314077
ER -