Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/1935
Title: Supervised and Unsupervised Machine Learning for Cancer Classification: Recent Development
Authors: Mazlan A.U. 
Sahabudin N.A.B. 
Remli, M.A. 
Ismail N.S.N. 
Mohamad M.S. 
Warif N.B.A. 
Keywords: Artificial intelligence;Cancer classification;Machine learning;Supervised;Unsupervised
Issue Date: Jun-2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Conference: 2021 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2021 - Proceedings 
Abstract: 
This is models with the ability to detect and classify cancer is important in the industrial of healthcare. The most difficult aspect for such model is the classification of cancer, which can be addressed using machine learning methods. The methods are used to improve classification accuracy between system output and test data. The classification process becomes more difficult due to vast data information. This paper presents an overview on current development of cancer classification techniques using machine learning methods, which have received increasing attention within the area of healthcare. This review will mainly focus on the development of machine learning methods for classification of cancer diseases. Recently, there are various researchers proposed different kinds of methods for cancer classification. The results show that the successful of cancer classification is dependent on the machine learning models. Besides, various types of healthcare data used in the experiments would also be discussed in this paper. The development of many optimization methods for cancer classification has brought a lot of improvement in the healthcare field. There is demand for further improvements in optimization methods to develop better machine learning models for cancer classification.
Description: 
Scopus
URI: http://hdl.handle.net/123456789/1935
ISBN: 978-166540343-6
DOI: 10.1109/I2CACIS52118.2021.9495888
Appears in Collections:Faculty of Data Science and Computing - Proceedings

Show full item record

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.