Artificial Intelligence and Machine Learning Based Models for Prediction and Treatment of Cardiovascular Diseases: A Review
Sreedevi Gandham1, Balaji Meriga2

1Dr. Sreedevi Gandham*, Associate Professor, Department of Electronics & Communication Engineering, Siddartha Educational Academy Group of Institutions, C. Gollapalli, Tirupati (A.P), India.
2Dr. Balaji Meriga, Associate Professor, Department of Biochemistry, Sri Venkateswara University, Tirupati (A.P), India. 

Manuscript received on 27 February 2022. | Revised Manuscript received on 31 March 2022. | Manuscript published on 30 May 2022. | PP: 35-40 | Volume-11 Issue-1, May 2022. | Retrieval Number: 100.1/ijrte.D66321110421 | DOI: 10.35940/ijrte.D6632.0511122
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Abstract: Advances in Machine Learning (ML) algorithms, computing and Artificial Intelligence (AI)-based systems have been gradually finding applications in several domains including medical and health care systems. By using big data analytics and machine learning methodologies, AI has become a promising tool in the diagnosis and treatment of cardiovascular diseases. AI-ML based applications enhance our understanding of different parameters and phenotypes of heart diseases and lead to newer therapeutic strategies to tackle different types of cardiovascular ailments, a newer approach to cardiovascular drug therapy and a post-marketing survey of prescription drugs. Although AI has wide range of applications, it is in infant stage and has certain limitations in the clinical use of results and their interpretations such as data privacy, selection bias etc, which may result in wrong conclusions. Thus, AI-ML is a transformative technology and has immense potential in health care systems. This review covers various aspects of cardiovascular diseases (CVDs) and illustrate AI and ML based methods including supervised, unsupervised and deep learning and their applications in cardiovascular imaging, cardiovascular risk prediction and newer drug targets. 
Keywords: Artificial Intelligence, Machine Learning, Big Data, Supervised Learning, Reinforced Learning.
Scope of the Article: Machine Learning.