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CARDIA: Comprehensive Assessment Resource for Diagnosing, Interpreting, and Addressing Heart Diseases
  • +2
  • Mahfuzul Haque,
  • Debashis Gupta,
  • Aditi Golder,
  • Jony Hossain,
  • Kishor Datta Gupta
Mahfuzul Haque
Department of Computer Science and Engineering, University of Science and Technology

Corresponding Author:[email protected]

Author Profile
Debashis Gupta
Department of Computer Science, Wake Forest University
Aditi Golder
Institute of Information Technology, Jahangirnagar University
Jony Hossain
Department of Cardiology, Zia Medical College Bogra
Kishor Datta Gupta
Department of Computer Science, Clark Atlanta University


Heart disease has been the leading cause of mortality globally. The necessity for quick access to trustworthy, dependable, and practical processes for early diagnosis and disease management pertains to numerous risk factors for heart disease. In the current global environment, detecting heart disease through early-onset manifestations is challenging. This has the potential to be fatal if not stopped in time. In isolated, semiurban, or rural locations without access to heart specialists, accurate risk prediction and analysis may be essential for the early detection of cardiac issues. Artificial Intelligence (AI) and robotics are currently used in medical research. This addresses the urgent need for better ways to find, diagnose, and treat heart disease. To close the gap between theory and reality, we offer a dataset on cardiovascular disease that has been carefully put together. The variables in the dataset are age, gender, subtypes, symptoms, risk factors, and result variables that can be either 1 or 0. The
11 Jan 2024Submitted to TechRxiv
22 Jan 2024Published in TechRxiv