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Rummage of Machine Learning Algorithms in Cancer Diagnosis

Prashant Johri, Vivek sen Saxena, Avneesh Kumar

Source Title: International Journal of E-Health and Medical Communications (IJEHMC)12(1)

ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781799861546|DOI: 10.4018/IJEHMC.2021010101

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MLA

Johri, Prashant, et al. "Rummage of Machine Learning Algorithms in Cancer Diagnosis." IJEHMC vol.12, no.1 2021: pp.1-15. http://doi.org/10.4018/IJEHMC.2021010101

APA

Johri, P., Saxena, V. S., & Kumar, A. (2021). Rummage of Machine Learning Algorithms in Cancer Diagnosis. International Journal of E-Health and Medical Communications (IJEHMC), 12(1), 1-15. http://doi.org/10.4018/IJEHMC.2021010101

Chicago

Johri, Prashant, Vivek sen Saxena, and Avneesh Kumar. "Rummage of Machine Learning Algorithms in Cancer Diagnosis," International Journal of E-Health and Medical Communications (IJEHMC) 12, no.1: 1-15. http://doi.org/10.4018/IJEHMC.2021010101

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Abstract

With the continuous improvement of digital imaging technology and rapid increase in the use of digital medical records in last decade, artificial intelligence has provided various techniques to analyze these data. Machine learning, a subset of artificial intelligence techniques, provides the ability to learn from past and present and to predict the future on the basis of data. Various AI-enabled support systems are designed by using machine learning algorithms in order to optimize and computerize the process of clinical decision making and to bring about a massive archetype change in the healthcare sector such as timely identification, revealing and treatment of disease, as well as outcome prediction. Machine learning algorithms are implemented in the healthcare sector and helped in diagnosis of critical illness such as cancer, neurology, cardiac, and kidney disease as well as with easing in anticipation of disease progression. By applying and executing machine learning algorithms over healthcare data, one can evaluate, analyze, and generate the results that can be used not only to advance the prior health studies but also to aid in forecasting a patient's chances of developing of various diseases. The aim in this article is to present an overview of machine learning and to cover various algorithms of machine learning and their present implementation in the healthcare sector.