Responsible CVD screening with a blockchain assisted chatbot powered by explainable AI.

Journal: Scientific reports
PMID:

Abstract

Cardiovascular disease (CVD) is rising as a significant concern for the healthcare sector around the world. Researchers have applied multiple traditional approaches to making healthcare systems find new solutions for the CVD concern. Artificial Intelligence (AI) and blockchain are emerging approaches that may be integrated into the healthcare sector to help responsible and secure decision-making in dealing with CVD concerns. Secure CVD information is needed while dealing with confidential patient healthcare data, especially with a decentralized blockchain technology (BCT) system that requires strong encryption. However, AI and blockchain-empowered approaches could make people trust the healthcare sector, mainly in diagnosing areas like cardiovascular care. This research proposed an explainable AI (XAI) approach entangled with BCT that enhances healthcare interpretability and responsibility to cardiovascular health medical experts. XAI is significant in addressing cardiovascular prediction issues and offers potential solutions for complex communication and decision-making in cardiovascular care. The proposed approach performs better, with the highest accuracy of 97.12% compared to earlier methods. This achievement shows its ability to tackle complex issues, accessible during healthcare sector communication and decision processes.

Authors

  • Salman Muneer
    Department of Computer Science, University of Central Punjab, Lahore, Pakistan.
  • Sagheer Abbas
    Department of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan.
  • Asghar Ali Shah
    Faculty of Engineering, Bahria University, Lahore Campus, Lahore, Pakistan.
  • Meshal Alharbi
    Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
  • Haya Aldossary
    Computer Science Department, College of Science and Humanities, Imam Abdulrahman Bin Faisal University, Jubail 31961, Saudi Arabia. Electronic address: Healdossary@iau.edu.sa.
  • Areej Fatima
    Department of Computer Science, Lahore Garrison University, Lahore 54792, Pakistan.
  • Taher M Ghazal
    Center for Cyber Security, Faculty of Information Science and Technology, University Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor, Malaysia.
  • Khan Muhammad Adnan
    Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, 13557, Republic of Korea. adnan@gachon.ac.kr.