Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Cancer Prognosis by Using Machine Learning and Data Science: A Systematic Review Subject The topic of the resource Artificial intelligence; Cancer; Data science; Decision trees; Deep learning; Machine learning Description An account of the resource 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. Creator An entity primarily responsible for making the resource Lakshmikanth Rajath Mohan T.; Jayapandian N. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-551, pp. 1-12. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-19-6631-6_1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-6631-6_1</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153110118&amp;doi=10.1007%2F978-981-19-6631-6_1&amp;partnerID=40&amp;md5=ab92fec2edb653dcaa8846ca8d5a2f72" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153110118&amp;doi=10.1007%2f978-981-19-6631-6_1&amp;partnerID=40&amp;md5=ab92fec2edb653dcaa8846ca8d5a2f72</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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