Abstract
Cancer has been described as a diverse illness with several distinct subtypes that may occur simultaneously. As a result, early detection and forecast of cancer types have graced essentially in cancer fact-finding methods since they may help to improve the clinical treatment of cancer survivors. The significance of categorizing cancer suffers into higher or lower-threat categories has prompted numerous fact-finding associates from the bioscience and genomics field to investigate the utilization of machine learning (ML) algorithms in cancer diagnosis and treatment. Because of this, these methods have been used with the goal of simulating the development and treatment of malignant diseases in humans. Furthermore, the capacity of machine learning techniques to identify important characteristics from complicated datasets demonstrates the significance of these technologies. These technologies include Bayesian networks and artificial neural networks, along with a number of other approaches. Decision Trees and Support Vector Machines which have already been extensively used in cancer research for the creation of predictive models, also lead to accurate decision making. The application of machine learning techniques may undoubtedly enhance our knowledge of cancer development; nevertheless, a sufficient degree of validation is required before these approaches can be considered for use in daily clinical practice. An overview of current machine learning approaches utilized in the simulation of cancer development is presented in this paper. All of the supervised machine learning approaches described here, along with a variety of input characteristics and data samples, are used to build the prediction models. In light of the increasing trend towards the use of machine learning methods in biomedical research, we offer the most current papers that have used these approaches to predict risk of cancer or patient outcomes in order to better understand cancer.
Highlights
The field of cancer research has seen a constant development over the last few decades
The most current work related to cancer prediction/prognosis using machine learning methods is given in various review articles
After providing a brief overview of the machine learning branch, as well as the techniques of data preprocessing approaches, feature selection methods, along with classification approaches, we presented 3 particular instances involving the forecasting of various cancers, cancer recurrence, and cancer survival using familiar machine learning tools
Summary
The field of cancer research has seen a constant development over the last few decades. Machine learning techniques have grown more popular among medical researchers These methods are capable of discovering and identifying patterns and connections between them in large datasets, and they are capable of accurately predicting the future results of a cancer type in humans. Numerous researches have been published in the survey, each of which is depending on a distinct strategy that may aid in the diagnosis and prognosis of cancer in its early stages. These papers discuss methods linked to the screening of moving miRNAs that was shown to be an encouraging category of molecules for. Researches which used ML approaches to forecast cancer prognosis along with treatment are the only ones that are discussed here
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