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An Overview and Analysis of Machine Learning Classification Algorithms in Healthcare

An Overview and Analysis of Machine Learning Classification Algorithms in Healthcare

Soumitra Saha
Copyright: © 2024 |Pages: 23
ISBN13: 9798369318225|ISBN13 Softcover: 9798369345047|EISBN13: 9798369318232
DOI: 10.4018/979-8-3693-1822-5.ch003
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MLA

Saha, Soumitra. "An Overview and Analysis of Machine Learning Classification Algorithms in Healthcare." Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods, edited by Umesh Kumar Lilhore, et al., IGI Global, 2024, pp. 39-61. https://doi.org/10.4018/979-8-3693-1822-5.ch003

APA

Saha, S. (2024). An Overview and Analysis of Machine Learning Classification Algorithms in Healthcare. In U. Lilhore, A. Kumar, S. Simaiya, N. Vyas, & V. Dutt (Eds.), Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods (pp. 39-61). IGI Global. https://doi.org/10.4018/979-8-3693-1822-5.ch003

Chicago

Saha, Soumitra. "An Overview and Analysis of Machine Learning Classification Algorithms in Healthcare." In Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods, edited by Umesh Kumar Lilhore, et al., 39-61. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-1822-5.ch003

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Abstract

To decode a wide range of complex and challenging problems around us, we must utilize the data that already exists in our surroundings as effectively as possible. This data will be functional in diverse fields in everyday life, and the data plays the most crucial role in healthcare and medicine, finance and banking, and information technology. The first, foremost, and prime reason for forming or generating this big data is the increasing complexity of real-world problems, which takes considerable work to implement. For example, in healthcare and medicine, extensive data are needed to detect deadly diseases like cancer, cardiovascular diseases, and HIV/AIDS effectively. Classification algorithms are the essential and substantial machine learning algorithms used to decode numerous challenging real-life problems in the healthcare industry. Implementing the algorithm encounters fewer problems regarding time and space and comparatively better interpretability and scalability. Through this study, the authors have demonstrated how different classification algorithms perform in the healthcare system.

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