Cardiovascular Disease Detection using Deep Learning

Author(s)	AADIT TRIVEDI, AARYA SHAH, Dr. M. L. Sworna Kokila
Country	India
Abstract	Cardiovascular diseases have surged as the leading global cause of death, necessitating a focus on early detection and continuous monitoring. This study employs deep learning, specifically the MobileNet Architecture, on a public ECG dataset to predict four cardiac abnormalities: abnormal heartbeat, myocardial infarction, history of myocardial infarction, and normal cases. The developed model demonstrates a notable training accuracy of 97.34% and validation accuracy of 91.00%, showcasing its efficacy in disease classification. With the potential to save lives and reduce healthcare costs, this algorithmic approach offers a reliable, time-efficient alternative to manual diagnosis in detecting heart disorders, providing valuable support to medical professionals..
Keywords	Cardiovascular diseases, deep learning, MobileNet Architecture, ECG dataset, early detection, disease classification, healthcare, medical professionals.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 6, Issue 1, January-February 2024
Published On	2024-02-28
DOI	https://doi.org/10.36948/ijfmr.2024.v06i01.13749

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