## 1 Introduction
Every attribute is assigned a rank by a scoring value. Using the ranking of attributes, the ones with the lowest ranking are eliminated from the dataset. The prediction accuracy of an induced data mining algorithm is utilized to evaluate an attribute subset in case of Wrappers. The performance of every subset of attributes is evaluated by the use of an induced classifier to the dataset with all attributes. Wrappers are implemented to generate a subset of attributes and to obtain high prediction accuracy rate. This is achieved by considering the most relevant attributes. A system model is trained by using an attribute subset that are inserted or deleted from the subset on the basis of evaluation of previous model. Therefore, it is a search problem that is computationally expensive. Figure 2 presents the wrapper model where a predictive approach is used to compute the attribute subsets. Unlike Wrappers, Filter techniques for attribute selection do not depend on an induction classifier. These techniques do not make an attribute subset. They simply used to evaluate each attribute independently and choose the best attribute set. The intrinsic properties of data samples are used for the evaluation of attribute subsets. Figure 3 shows the filter approach for attribute selection. Figure 1: Attribute Selection Process. Figure 2: Wrapper Approach for Attribute Selection. Attributes are characterized by their relevance with respect to target class. Since induction is not applied to determine the attribute subset, the prediction accuracy rate is comparatively lower than that of Wrappers. Filters are more generalized than Wrappers since they do not depend on any induction technique for classification. In Filters, the attributes are selected on the basis of their ranking achieved in several test conditions for their association with the resultant labelled class. This attribute selection technique can be implemented for effective disease diagnosis [11]. Inconsistencies are usually detected in the collected chronic disease datasets. Datasets of diseases such as hepatitis, lung- and heart-related diseases, breast cancer and diabetes are highly unstructured. This means that the data require pre-processing which will remove redundant and noisy features [11]. The presence of noisy features and outliers hampers the classification mining task. Sometimes, not all the symptoms present in the datasets are required for the disease classification task. Thus, the less relevant symptoms should be excluded from the final datasets. By selecting the most relevant attributes, it is possible to improve accuracy and reduce processing time [12]. In addition, in some situations, the structure of attributes is difficult to interpret. A suitable attribute optimization approach is need for accurate the accurate treatment of diseases. The main objective of this work is to analyse popular attribute selection techniques and the impact of filter and wrapper approaches on the evaluation of the classification of chronic diseases from datasets. Common chronic disease datasets like diabetes, heart disease and breast cancer are used to determine the impact of filters and wrappers on classification performance. The benefits and limitations of wrappers and filters are presented through experimental demonstration using the WEKA software [13], Due to its ease of use and versatile nature, decision tree is used as classifier in the research. On the one hand, correlation feature selection (CFS), information gain (IG) and chi-square are used as filters in analysis. On the other hand, the best first search (BFS), greedy step wise search (GSS) and linear forward selection (LFS) methods are the wrappers used. Ranker algorithm is the predefined search scheme applied to filter approach while WrapperSubsetEval is the predefined search method used in combination with wrapper methods.