Analysis, Discussion, and Evaluations for the Case Studies

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Copyright: 2021
Pages: 9
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.ch006

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

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Abstract

The purpose of this chapter is to discuss and analyse the results produced in Chapter 5. To evaluate the proposed models, this chapter compares the models with others existing in the literature. Additionally, the chapter discusses the evaluation measures used to validate the experimental results of Chapter 5. For example, from experiments, GA/DT demonstrated the highest average accuracy (92%) for classifying colon cancer, compared with other algorithms. PSO/DT presented 89%, PSO/SVM presented 89%, and IG/DT presented 89%, demonstrating very good classification accuracy. PSO/NB presented 57% and GA/NB presented 58%: less classification accuracy. Table ‎6.1 lists all accuracies resulting from experiments of case study one, as applied to the full data set. There are 45 algorithmic incorporation methods that have accuracy above 80% when applied to the full dataset. One algorithm presents an accuracy of 92%. Nine others scored below 60%.

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Analysis, Discussion, and Evaluations for the Case Studies

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