Detection of Heart DiseasesUsingArtificialIntelligence
Session
Computer Science and Communication Engineering
Description
Cardiovascular diseases, particularly the late diagnosis of heart problems, are the leading cause of mortality globally, imposing a significant health and economic burden on society. This research explores the potential of artificial intelligence to transform the diagnosis of heart diseases, focusing on the use of convolutional neural networks (CNN) and other machine learning algorithms to enhance predictive accuracy and reliability. The study utilizes clinical data, including electrocardiograms (ECG) and medical images, to develop and evaluate AI models capable of early detection and accurate diagnosis of various heart conditions. The results show that AI models significantly improve diagnostic performance, facilitating timely medical interventions and better outcomes for patients. This research contributes to the body of knowledge on AI applications in healthcare, aiming to pave the way for future innovations in cardiovascular diagnostics and patient care.
Keywords:
Artificial Intelligence, Customer Relationship Management, Communication System
Proceedings Editor
Edmond Hajrizi
ISBN
978-9951-982-15-3
Location
UBT Kampus, Lipjan
Start Date
25-10-2024 9:00 AM
End Date
27-10-2024 6:00 PM
DOI
10.33107/ubt-ic.2024.393
Recommended Citation
Zefaj, Engelbert and Murtezaj, Altin, "Detection of Heart DiseasesUsingArtificialIntelligence" (2024). UBT International Conference. 9.
https://knowledgecenter.ubt-uni.net/conference/2024UBTIC/CS/9
Detection of Heart DiseasesUsingArtificialIntelligence
UBT Kampus, Lipjan
Cardiovascular diseases, particularly the late diagnosis of heart problems, are the leading cause of mortality globally, imposing a significant health and economic burden on society. This research explores the potential of artificial intelligence to transform the diagnosis of heart diseases, focusing on the use of convolutional neural networks (CNN) and other machine learning algorithms to enhance predictive accuracy and reliability. The study utilizes clinical data, including electrocardiograms (ECG) and medical images, to develop and evaluate AI models capable of early detection and accurate diagnosis of various heart conditions. The results show that AI models significantly improve diagnostic performance, facilitating timely medical interventions and better outcomes for patients. This research contributes to the body of knowledge on AI applications in healthcare, aiming to pave the way for future innovations in cardiovascular diagnostics and patient care.