AN AUTOMATIC IDENTIFICATION OF CARDIAC VASCULAR DISEASE FOR UNPREDICTED CLIMATE CHANGE USING DEEP NEURAL NETWORKS
Abstract
The automated identity of Cardiac Vascular sickness for Unpredictable climate change the use of Deep Neural Networks involves the usage of gadget mastering (ML) strategies to allow the actual-time monitoring of the consumer’s cardiovascular health. This approach makes use of deep learning-based totally algorithms and the present medical data to diagnose the risk of the cardiovascular disease (CVD). The deep neural community (DNN) is trained with the existing scientific facts to discover the danger of CVD. The DNN is then used to perceive the danger factors of CVD by analyzing the patient’s clinical facts. The deep getting to know system will also be used to generate the alerts and warnings when the patient’s CVD danger ranges trade. This alert and warnings might be used by the scientific practitioners to intervene and provide the remedy for the patient. In addition, the AI-driven system will enable the affected person to higher control their medical fitness and are expecting the chance of CVD.

Authors
L. Godlin Atlas, K.V. Shiny, D. Bhavana, J. Lethisia Nithiya
Bharath Institute of Higher Education and Research, India

Keywords
Unpredictable, Generate, Monitoring, Practitioners, Intervene
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Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 4 , Issue: 4 , Pages: 505 - 511 )
Date of Publication :
September 2023
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608
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