Cancer Prognosis by Using Machine Learning and Data Science: A Systematic Review

Title

Cancer Prognosis by Using Machine Learning and Data Science: A Systematic Review

Creator

Lakshmikanth Rajath Mohan T.; Jayapandian N.

Description

Cancer is one of the most fatal diseases in the world and the leading cause for most deaths worldwide. Diagnosing cancer early has become the need of the day for doctors and researchers as it allows them to categorize patients as high-risk and low-risk categories which will eventually help them in correct diagnosis and treatment. Machine learning is a subset of artificial intelligence that makes use of raw data to make predictions and insights. Using machine learning for cancer prognosis has been under study for a long time and several papers have been published regarding the same. Even though many papers have been published on the usage of statistical methods for cancer prognosis, it has been proved that machine learning models provide more accuracy when compared to conventional statistical methods of detection. These machines can be trained to detect abnormalities such as a tumour by looking at real-world examples. Models such as artificial neural networks, decision trees, clustering techniques, and K-Nearest-Neighbours (KNNs) are being used for cancer prediction, prognosis and also research purposes. The key aim of this article is to go through the popular key trends in using machine learning algorithms for cancer prognosis, types of input datasets to be fed, different types of cancers that can be studied and also the performance of these models. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Networks and Systems, Vol-551, pp. 1-12.

Date

2023-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Artificial intelligence; Cancer; Data science; Decision trees; Deep learning; Machine learning

Coverage

Lakshmikanth Rajath Mohan T., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Kengeri Campus, Bangalore, India; Jayapandian N., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Kengeri Campus, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23673370

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1007/978-981-19-6631-6_1

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153110118&doi=10.1007%2f978-981-19-6631-6_1&partnerID=40&md5=ab92fec2edb653dcaa8846ca8d5a2f72