文章基本信息

标题：CLASSIFICATION OF PNEUMONIA PATIENTS RISK USING HYBRID GENETIC ALGORITHM-DISCRIMINANT ANALYSIS AND NA�VE BAYES
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作者：IRHAMAH ; SITI MAR’ATUS RAHIMATIN ; HERI KUSWANTO 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2019
卷号：97
期号：6
页码：1845-1855
出版社：Journal of Theoretical and Applied
摘要：Pneumonia is the most common causes of death in developing countries, such as in Indonesia. Therefore, appropriate pneumonia classification is very important in determining the disease severity and to know the most appropriate treatment for the patient. In this study, Discriminant Analysis (DA), hybrid Genetic Algorithm- Discriminant Analysis (HGA-DA) and Na�ve Bayes (NB) are used to classify risk class of patient. GA is an artificial intelligent method that can avoid a trap in local optima and easy to implement in solving various objective functions and constraints, while NB is a simple but powerful method that returns not only prediction but also the degree of certainty. In this study, GA is used to improve multi-class classification performance of DA. Firstly, GA is used for variable selection in DA, and then a comparative study with other variable selection methods is performed. In addition, Genetic Algorithm is also implemented for parameter estimation. Analysis results show that there are differences in selected variables from four selection methods in classifying patient risk class. The use of hybrid methods of DA and GA in variable selection and parameter optimization stages gives better multi-class classification results than DA or NB, since it produces highest Geometric Mean (GM) and Area Under Curve (AUC) criterion.
关键词：Pneumonia; Multi-class Classification; Discriminant Analysis; Genetic Algorithm; Na�ve Bayes