Final Remarks for the Research With Advanced Machine Learning Methods in Colon Cancer Analysis

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Copyright: 2021
Pages: 4
Source title: Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis
Source Author(s)/Editor(s): Zhongyu Lu (University of Huddersfield, UK), Qiang Xu (University of Huddersfield, UK), Murad Al-Rajab (University of Huddersfield, UK & Abu Dhabi University, UAE)and Lamogha Chiazor (University of Huddersfield, UK)
DOI: 10.4018/978-1-7998-7316-7.ch007

Keywords: Knowledge Management and Library Science / Machine Learning / Medical Diagnosis and Practice / Medical Information Science Reference

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Abstract

Generally, classification accuracy is very important to gene processing and selection and cancer classification. It is needed to achieve better cancer treatments and improve medical drug assignments. However, the time complexity analysis will enhance the application's significance. To answer the research questions in Chapter 1, several case studies have been implemented (see Chapters 4 and 5), each was essential to sustain the methodologies discussed in Chapter 3. The study used a colon-cancer dataset comprising 2000 genes. The best search algorithm, GA, showed high performance with a good efficient time complexity. However, both DTs and SVMs showed the best classification contribution with reference to performance accuracy and time efficiency. However, it is difficult to apply a completely fair comparative study because existing algorithms and methods were tested by different authors to reflect the effectiveness and powerful of their own methods.

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Final Remarks for the Research With Advanced Machine Learning Methods in Colon Cancer Analysis

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