Applications of Machine learning and its advantages: A review

Jaya Khanan

Abstract


Data analytics advancements have become a disruptive force in a variety of disciplines; their effect has changed the dynamics of previously technology-supported fields, transforming them into technology-dependent ones. Healthcare is one such sector where this influence is still in its early stages. Due to the data-hungry nature of machine learning algorithms, trends in artificial intelligence have only reached their full potential when backed up by large datasets. Our article examines the implications of recent advancements in data analytics and how they may be used in the healthcare industry, with a focus on predictive and visualization applications.

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MachineLearning: What it is and why it matters, 09 2016, [online] Available: www.sas.com.

R. E. Schapire, "The boosting approach to machine learning: An overview" in Nonlinear estimation and classification, New York:Springer, pp. 149-171, 2003.

J. M. Jerez, I. Molina, P. J. García-Laencina, E. Alba, N. Ribelles, M. Martín, et al., "Missing data imputation using statistical and machine learning methods in a real breast cancer problem",

Artificial intelligence in medicine, vol. 50, no. 2, pp. 105-115, 2010.

A. Ben-Hur, D. Horn, H. T. Siegelmann and V. Vapnik,

"Support vector clustering", Journal of machine learning research, vol. 2, pp. 125-137, Dec 2001.

F. Min, Q. Hu and W. Zhu, "Feature selection with test cost constraint", International Journal of Approximate Reasoning, vol. 55, no. 1, pp. 167-179, 2014.

Show in Context CrossRef Google Scholar 6.J. R. Otukei and T. Blaschke, "Land cover change assessment using decision trees support vector machines and maximum likelihood classification algorithms", International Journal of Applied Earth Observation and Geoinformation, vol. 12, pp. S27-S31, 2010.

Kubat, R. C. Holte and S. Matwin, "Machine learning for the detection of oil spills in satellite radar images", Machine learning, vol. 30, no. 2–3, pp. 190-215, 1998.

Chyi IH and Yang MJ (2009) Is online news an inferior good? Examining the economic nature of online news among users. Journalism and Mass Communication Quarterly 86(3): 594–612.

Converse PE (1968) Time budgets. In: Sills D (ed.) International Encyclopedia of the Social Sciences. New York: Macmillan, pp. 42–47.

Dayan D and Katz E (1992) Media Events: The Live Broadcasting of History. Cambridge, MA: Harvard University Press.

De Grazia S (1962) Of Time, Work, and Leisure. New York: Twentieth Century Fund.


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