Prediction of Cancer Disease using Machine learning Approach

Cancer has identified a diverse condition of several various subtypes. The timely screening and course of treatment of a cancer form is now a requirement in early cancer research because it supports the medical treatment of patients. Many research teams studied the application of ML and Deep Learning methods in the field of biomedicine and bioinformatics in the classification of people with cancer across high-or low risk categories. These techniques have therefore been used as a model for the development and treatment of cancer. As, it is important that ML instruments are capable of detecting key features from complex datasets. Many of these methods are widely used for the development of predictive models for predicating a cure for cancer, some of the methods are artificial neural networks (ANNs), support vector machine (SVMs) and decision trees (DTs). While we can understand cancer progression with the use of ML methods, an adequate validity level is needed to take these methods into consideration in clinical practice every day. In this study, the ML & DL approaches used in cancer progression modeling are reviewed. The predictions addressed are mostly linked to specific ML, input, and data samples supervision. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Virtual Conference on Advanced Nanomaterials and Applications. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).