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
Cite This	Cardiovascular Disease Detection using Deep Learning - AADIT TRIVEDI, AARYA SHAH, Dr. M. L. Sworna Kokila - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.13749
DOI	https://doi.org/10.36948/ijfmr.2024.v06i01.13749
Short DOI	https://doi.org/gtktjf

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research