Machine Intelligence-Based Trend Analysis of COVID-19 for Total Daily Confirmed Cases in Asia and Africa

Meslie, Yibeltal; Enbeyle, Wegayehu; Pandey, Binay Kumar; Pramanik, Sabyasachi; Pandey, Digvijay; Dadeech, Pankaj; Belay, Assaye; Saini, Ashwini Kumar

doi:10.4018/978-1-7998-7701-1.ch009

Cited by 59 publications

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“…Evidently, the use of machine learning techniques to cancer responsiveness, repetition, and durability forecast may enhance the precision of cancer risk and survival predictions. According to [1], the reliability of cancer's realization has increased by 15 percent to 20 percent in the past few years as a result of the use of machine learning [2][3] methods.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Frameworks in Cancer Detection

2021

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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.

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Section: Introductionmentioning

confidence: 99%