APDRChain: ANN Based Predictive Analysis of Diseases and Report Sharing Through Blockchain

  • Snehasis Bagchi
  • Mohuya ChakrabortyEmail author
  • Arup Kumar Chattopadhyay
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1065)


A huge amount of healthcare data (structured and unstructured) is currently available to medical specialists, indicating details of clinical symptoms. Each type of data provides information that must be properly analyzed for healthcare diagnosis. To simplify the diagnostic process, avoid misdiagnosis as well as early detection, artificial intelligence (AI) that aims to mimic human cognitive functions may be employed. Current AI techniques that are used for structured data include machine learning methods, such as the classical support vector machine, artificial neural network, and the modern deep learning. Natural language processing is mainly used for unstructured data. In this paper, we have adopted artificial neural network by using adaptive learning algorithms to handle diverse types of cardiovascular clinical data and integrate them into categorized major cardiovascular disease outputs such as heart failure, aortic aneurysm, cardiomyopathy, cerebrovascular disease, etc. These outputs are then shared as reports to patients as well as doctors by an efficient report sharing scheme called APDRChain, which combines blockchain and structured peer-to-peer network techniques with clever cryptography to create a consensus mechanism. The evaluation results show that APDRChain can achieve higher efficiency and satisfy the security requirements in report sharing.


Blockchain Cryptography Artificial neural network Healthcare data Report sharing Medical diagnosis 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Snehasis Bagchi
    • 1
  • Mohuya Chakraborty
    • 1
    Email author
  • Arup Kumar Chattopadhyay
    • 1
  1. 1.Institute of Engineering and ManagementKolkataIndia

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