Rummage of Machine Learning Algorithms in Cancer Diagnosis

Rummage of Machine Learning Algorithms in Cancer Diagnosis

Prashant Johri, Vivek sen Saxena, Avneesh Kumar
Copyright: © 2021 |Pages: 15
DOI: 10.4018/IJEHMC.2021010101
Article PDF Download
Open access articles are freely available for download


With the continuous improvement of digital imaging technology and rapid increase in the use of digital medical records in last decade, artificial intelligence has provided various techniques to analyze these data. Machine learning, a subset of artificial intelligence techniques, provides the ability to learn from past and present and to predict the future on the basis of data. Various AI-enabled support systems are designed by using machine learning algorithms in order to optimize and computerize the process of clinical decision making and to bring about a massive archetype change in the healthcare sector such as timely identification, revealing and treatment of disease, as well as outcome prediction. Machine learning algorithms are implemented in the healthcare sector and helped in diagnosis of critical illness such as cancer, neurology, cardiac, and kidney disease as well as with easing in anticipation of disease progression. By applying and executing machine learning algorithms over healthcare data, one can evaluate, analyze, and generate the results that can be used not only to advance the prior health studies but also to aid in forecasting a patient's chances of developing of various diseases. The aim in this article is to present an overview of machine learning and to cover various algorithms of machine learning and their present implementation in the healthcare sector.
Article Preview

1. Introduction

Machine learning, a subfield of Artificial Intelligence, provides algorithms to learn from past experiments while performing a particular task and measuring performance. By working continuously on a task, the performance of the task gets improved and the user experience as well. A Machine learning system has a training data set working as knowledge base and rules for decision making (Blum 2007). Machine learning is the building and exploring of methods in a computer programming language and making them “learn”. The program developed using machine learning algorithms accesses the data, trains the machine and tests it again for performance evaluation. The most important characteristic of machine learning is its ability to forecast. In machine learning, a model for prediction is build by existing information and it is further used for predicting the data. The major aspect of learning is the features selection from the data set as all the features cannot be used in learning. The data set may have multiple fields and perspectives. The selection of features is done according to their relevance, implication and scenario (Du & Swamy 2013). The primary goal of machine learning is to produce and enhance the learning algorithms and models in order to facilitate their easy application in various disciplines such as agriculture (Patrício & Rieder 2018), banking (Erdogan 2013), cyber security (Buczak & Guven 2016), economics (Einav & Levin 2014), finance (Lin et al., 2012), insurance (Gan 2013), natural language processing (Collobert & Weston 2008), online & traditional marketing (Tripathy et al., 2006), healthcare (Crown 2015), network & telecommunication (Richter & Khoshgoftaar 2018) and others as well.

Complete Article List

Search this Journal:
Volume 15: 1 Issue (2024)
Volume 14: 1 Issue (2023)
Volume 13: 5 Issues (2022): 4 Released, 1 Forthcoming
Volume 12: 6 Issues (2021)
Volume 11: 4 Issues (2020)
Volume 10: 4 Issues (2019)
Volume 9: 4 Issues (2018)
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing