Abstract
Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications.
Highlights
In the last decades, the production of huge amounts of data is rapidly growing.Machine and computers have become an important aspect of technology in manipulating and extracting meaningful insight into the data
This paper mainly focuses on discovering recent developments of machine learning (ML) and deep learning (DL) methods for cancer classification
Classification problems in the gene expression dataset have largely been studied by researchers in the areas of machine learning and statistics
Summary
Aina Umairah Mazlan 1 , Noor Azida Sahabudin 1, * , Muhammad Akmal Remli 2,3, *, Nor Syahidatul Nadiah Ismail 1 , Mohd Saberi Mohamad 4 , Hui Wen Nies 5 and Nor Bakiah Abd Warif 6.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have