Version 1
: Received: 23 August 2023 / Approved: 24 August 2023 / Online: 28 August 2023 (10:28:41 CEST)
How to cite:
Polanco, C.; Posadas-Sánchez, R.; Vargas-Alarcón, G. A Comprehensive Analysis of the Latest Automatic Applications of Artificial Intelligence in the Diagnosis of Cardiovascular Diseases. Preprints2023, 2023081777. https://doi.org/10.20944/preprints202308.1777.v1
Polanco, C.; Posadas-Sánchez, R.; Vargas-Alarcón, G. A Comprehensive Analysis of the Latest Automatic Applications of Artificial Intelligence in the Diagnosis of Cardiovascular Diseases. Preprints 2023, 2023081777. https://doi.org/10.20944/preprints202308.1777.v1
Polanco, C.; Posadas-Sánchez, R.; Vargas-Alarcón, G. A Comprehensive Analysis of the Latest Automatic Applications of Artificial Intelligence in the Diagnosis of Cardiovascular Diseases. Preprints2023, 2023081777. https://doi.org/10.20944/preprints202308.1777.v1
APA Style
Polanco, C., Posadas-Sánchez, R., & Vargas-Alarcón, G. (2023). A Comprehensive Analysis of the Latest Automatic Applications of Artificial Intelligence in the Diagnosis of Cardiovascular Diseases. Preprints. https://doi.org/10.20944/preprints202308.1777.v1
Chicago/Turabian Style
Polanco, C., Rosalinda Posadas-Sánchez and Gilberto Vargas-Alarcón. 2023 "A Comprehensive Analysis of the Latest Automatic Applications of Artificial Intelligence in the Diagnosis of Cardiovascular Diseases" Preprints. https://doi.org/10.20944/preprints202308.1777.v1
Abstract
The definition of artificial intelligence (AI) is the capacity of a computer or machine to mimic or simulate human intelligence. AI has been applied in different areas, including medicine. In cardiology, AI techniques have revolutionized the field of cardiac diagnosis, enabling the detection and prediction of cardiovascular diseases to be more accurate and efficient. This article provides an overview of machine learning, deep learning, and neural networks as they pertain to cardiac diagnosis. We will investigate how these techniques harness the power of data and algorithms to analyze complex patterns in cardiac data, thereby facilitating early detection, risk assessment, and treatment decision-making.
Keywords
Artificial intelligence; cardiology; deep learning; diagnosis; machine learning; neural networks
Subject
Public Health and Healthcare, Health Policy and Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.