Filter Based Feature Selection Methods for Prediction of Risks in Hepatitis Disease

Abstract

Abstract-Recently, large amount of data is widely available in information systems and data mining has attracted a big attention to researchers to turn such data into useful knowledge. This implies the existence of low quality, unreliable, redundant and noisy data which negatively affect the process of observing knowledge and useful pattern. Therefore, researchers need relevant data from huge records using feature selection methods. Feature selection is the process of identifying the most relevant attributes and removing the redundant and irrelevant attributes. In this study, a comparison between filter based feature selection methods based on a well-known dataset (i.e., hepatitis dataset) was carried out and four classification algorithms were used to evaluate the performance of the algorithms. Among the algorithms, Naï ve Bayes and Decision I. INTRODUCTIONRecently, thanks to innovations of computer and information technologies, huge amounts of data can be obtained and stored in both scientific and business transactions. This amount of data implies low quality, unreliable, redundant and noisy data to observe useful patterns [1]. Therefore, researchers need relevant and highquality data from huge records using feature selection methods.Feature selection methods reduce the dimensionality of feature space, remove redundant, irrelevant or noisy data. It brings the immediate effects for application: speeding up a data mining algorithm, improving the data quality and the performance of data mining and increasing the comprehensibility of the mining results [2].In this study, the great interest of hepatitis disease was considered which is a serious health problem in the world and a comparative analysis of several filter based selection algorithms was carried out based on the performance of four classification algorithms for the prediction of disease risks [3]. The main aim of this study is to make contributions in the prediction of hepatitis disease for medical research and introduce a detailed and comprehensive comparison of Manuscript received November 5, 2014; revised February 20, 2015. Pinar Yildirim is with the Okan University, Istanbul, Turkey (e-mail: pinar.yildirim@ okan.edu.tr). popular filter based feature selection methods. II. FEATURE SELECTION METHODSSeveral feature selection methods have been introduced in the machine learning domain. The main aim of these techniques is to remove irrelevant or redundant features from the dataset. Feature selection methods have two categories: wrapper and filter. The wrapper evaluates and selects attributes based on accuracy estimates by the target learning algorithm. Using a certain learning algorithm, wrapper basically searches the feature space by omitting some features and testing the impact of feature omission on the prediction metrics. The feature that make significant difference in learning process implies it does matter and should be considered as a high quality feature. On the other hand, filter uses the general characteristics of data itself and work separatel...

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