Feature selection/dimensionality reduction

Title

Feature selection/dimensionality reduction

Creator

Stephen D.; Aswathy S.U.; Sajan P.P.; Thomas J.

Description

In today's world, medical image analysis is a critical component of research, and it has been extensively explored over the last few decades. Machine learning in healthcare is a fantastic advancement that will improve disease detection efficiency and accuracy. In many circumstances, it will also allow for early detection and treatment in remote or developing areas. The amount of medical data created by various applications is growing all the time, creating a bottleneck for analysis and necessitating the use of a machine learning method for feature selection and dimensionality reduction techniques. Feature selection is an important concept of machine learning since it affects the model's performance and the data parameters you utilize to train your machine learning models to have a big influence on the performance. The approach of minimizing the number of inputs in training data by reducing the dimension of your feature set is known as dimensionality reduction. Reduced dimensionality aids in the overall performance of the machine learning algorithms. 2023 River Publishers.

Source

Machine Learning for Healthcare Systems: Foundations and Applications, pp. 169-185.

Date

2023-01-01

Publisher

River Publishers

Coverage

Stephen D., Department of Computer Science and Engineering, Jyothi Engineering College, India; Aswathy S.U., Department of Computer Science and Engineering, Jyothi Engineering College, India; Sajan P.P., C-DAC, India; Thomas J., Department of Computer Science and Engineering, Christ University, India

Rights

Restricted Access

Relation

ISBN: 978-877022810-7; 978-877022811-4

Format

Online

Language

English

Type

Book chapter

Identifier

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160591199&partnerID=40&md5=ad6daf0ce822803ac2f8c19b60d9c61d