Developing an Effective Classification Model for Medical Data Analysis

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Author(s): Naeem Ahmed Mahoto (Mehran University of Engineering and Technology, Pakistan)and Abdul Hafeez Babar (Mehran University of Engineering and Technology, Pakistan)
Copyright: 2019
Pages: 17
Source title: Advanced Classification Techniques for Healthcare Analysis
Source Author(s)/Editor(s): Chinmay Chakraborty (Birla Institute of Technology Mesra, India)
DOI: 10.4018/978-1-5225-7796-6.ch001

Keywords: Data Analysis & Statistics / Library & Information Science / Medical Information Science Reference / Medical Technologies

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Abstract

The sparse nature of medical data makes knowledge discovery and prediction a complex task for analysis. Machine learning algorithms have produced promising results for diversified data. This chapter constructs the effective classification model for medical data analysis. In particular, nine classification models, namely Naïve Bayes, decision tree (i.e., J48 and Random Forest), multilayer perceptron, radial bias function, k-nearest neighbors, single conjunctive rule learner, support vector machine, and simple logistics have been applied for developing an effective model. Besides, classification models have also been used in conjunction with ensemble learning methods, since ensemble methods significantly increase the predictive outcomes of the classification models. The evaluation of classification models has been measured using accuracy, f-measure, precision, and recall metrics. The empirical results revealed that the combination of ensemble learning methods with classification models produces better predictions in comparison with sole classification model for the medical data.

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Developing an Effective Classification Model for Medical Data Analysis

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