Details

Autor(en) / Beteiligte

• Chris Collin, J
• Jenefa, A
• Edward Naveen, V.
• Santhiya, P.
• Kuriakose, Bessy M
• Lincy, A

Titel

CardioPredict:Advancing Cardiovascular Diagnostics with Deep Learning

Ist Teil von

• 2024 Second International Conference on Data Science and Information System (ICDSIS), 2024, p.1-6

Ort / Verlag

IEEE

Erscheinungsjahr

2024

Quelle

IEEE Explore

Beschreibungen/Notizen

• Cardiovascular diseases (CVDs) stand as the foremost cause of death globally, highlighting the paramount importance of precise diagnostic techniques. Traditional diagnostic approaches, foundational yet challenged by interpretive variability and extensive time requirements, depend on the manual analysis of of electrocardiogram (ECG) images by medical professionals, introducing inconsistency in results. This study introduces "CardioPredict," a deep learning framework aimed at automating and refining the accuracy of CVD diagnoses through sophisticated ECG image analysis. "CardioPredict" harnesses an extensive dataset derived from the MIT-BIH Arrhythmia and PTB Diagnostic ECG databases, encompassing more than 48 hours of ECG recordings meticulously annotated with a diverse spectrum of cardiac conditions. Experimental outcomes reveal "CardioPredict" markedly surpasses traditional machine learning techniques, achieving an accuracy of 91.2%, precision of 91.8%, and recall of 90.5%. These outcomes demonstrate the model's exceptional performance in identifying diverse cardiovascular abnormalities, establishing a new benchmark for deep learning in medical imaging analysis. By automating ECG image interpretation, this project offers a pathway towards more dependable, accessible, and swift diagnoses of cardiovascular diseases, significantly enhancing patient care and management.