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Machine Learning in Cardiovascular Disorders

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Machine Learning in Biological Sciences

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Abstract

Cardiovascular Diseases, affecting human lives and claiming lives globally is a matter of concern globally. Although conventional methods are in use, machine learning applications and advances in radiomics and image based studies have enabled the development of noninvasive methods of detection, better and accurate detection, classification and risk stratification, diagnosis and prognosis in CVD, and the study of disorders with risks. We discuss in this chapter the different applications of machine learning in the domain of CVD.

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Abbreviations

ECG:

Electrocardiography

CV:

Cardiovascular

USG:

Ultrasonography

TNF-α:

Tissue necrosis factor-α

IL-2:

Interleukin-2

NT-proBNP:

N-Terminal pro-B-type natriuretic peptide

NLP:

Natural language processing

HER:

Electronic health records

ML:

Machine learning

CMR:

Cardiovascular magnetic resonance

CT:

Computerized tomography

RF:

Random Forest

PWV:

Pulse velocity wave

RA:

Rheumatoid arthritis

CCTA:

Coronary CT angiography

AAA:

Abdominal aortic aneurysm

iPSc:

Induced pluripotent stem cells

LQT:

Long QT syndrome (LQTS)

HCM:

Hypertrophic cardiomyopathy

ACHD:

Adult congenital heart disease

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Authors and Affiliations

  1. School of Biological Sciences, National Institute of Science Education and Research (NISER), Bhubaneswar, Odisha, India

    Shyamasree Ghosh

  2. Heathcare, Data Core Systems, Philadelphia, PA, USA

    Rathi Dasgupta

Authors
  1. Shyamasree Ghosh
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  2. Rathi Dasgupta
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Ghosh, S., Dasgupta, R. (2022). Machine Learning in Cardiovascular Disorders. In: Machine Learning in Biological Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-16-8881-2_13

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