TY - GEN
T1 - Supervised and Unsupervised Machine Learning for Cancer Classification
T2 - 2021 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2021
AU - Mazlan, Aina Umairah
AU - Sahabudin, Noor Azida binti
AU - Remli, Muhammad Akmal
AU - Ismail, Nor Syahidatul Nadiah
AU - Mohamad, Mohd Saberi
AU - Warif, Nor Bakiah Abd
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/26
Y1 - 2021/6/26
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Cancer classification
KW - Machine learning
KW - Supervised
KW - Unsupervised
UR - http://www.scopus.com/inward/record.url?scp=85112534717&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85112534717&partnerID=8YFLogxK
U2 - 10.1109/I2CACIS52118.2021.9495888
DO - 10.1109/I2CACIS52118.2021.9495888
M3 - Conference contribution
AN - SCOPUS:85112534717
T3 - 2021 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2021 - Proceedings
SP - 392
EP - 395
BT - 2021 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 June 2021
ER -