Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare

Prediction Problems in Healthcare Applications

Author(s): Boran Sekeroglu * .

Pp: 21-39 (19)

DOI: 10.2174/9781681088716121010005

* (Excluding Mailing and Handling)


Artificial intelligence tries to imitate human intelligence with powerful computer skills and aims to solve the problems that people have difficulty solving. Therefore, artificial intelligence and machine learning have begun to be applied thoughtfully in the field of healthcare and other lives. Remarkable results have been obtained in the performed research. These results have paved the way for applications in different healthcare areas and increase the frequency of the studies to achieve even more successful results in the same field. Prediction applications of machine learning are widely applied in healthcare as both class prediction and regression and support doctors and independent decision-making mechanisms. Even if classification studies of these applications have been performed, their diversity and excess numbers make them difficult and cause them to be considered on a subject basis. In this chapter, the usage of the terms “prediction,” “regression,” and “classification” in the literature is explained, and evaluation metrics used in all kinds of problem domains are defined. In addition to these, the problem areas of using machine learning techniques are summarized, and the literature search is performed in three scientific databases. Finally, the number of publications in the considered databases and an overview of the healthcare examples are presented. The data obtained and presented show that the applications using machine learning continue to increase significantly in healthcare and continue to be applied unlimitedly in all healthcare problems.

Keywords: Artificial intelligence, Classification, Evaluation metrics, Healthcare, Literature search, Machine learning algorithms, Machine learning, Prediction applications, Prediction, Regression.

Related Journals
Related Books
© 2024 Bentham Science Publishers | Privacy Policy