Article Preview
Top1. Introduction
Data mining is the subfield in knowledge management. Data mining helps in healthcare for effective treatment, healthcare management, customer relation management, fraud and mistreatment detection and decision making (Silwattananusarn & Tuamsuk, 2012). In health care, Heart disease has considerably amplified for the past ten years and has become the foremost reason of death for people in most countries around the world. The structure or the function of the heart gets distressed by many characteristics of these heart diseases (Chitra & Seenivasagam, 2013). Computer program acknowledged as Medical Decision-Support System was anticipated to support health professionals formulate medical decision (Shortliffe, 1987).
In disease forecast, feature extraction and selection are important steps. An optimum feature set must have resourceful and perceptive characteristics; and also reduce the redundancy of features to avoid “curse of dimensionality” issue (Osareh & Shadgar, 2011). The impact of unrelated features on the presentation of classifier systems can be scrutinized by feature selection strategies (Acir, Ozdamar, & Guzelis, 2006; Valentini, Muselli, & Ruffino, 2004)). In this phase an optimal subset of features that are necessary are selected. By lessening the dimensionality and ignoring unrelated features, feature selection develops the exactness of algorithms (Zhang, Guo Du, & Li, 2005; Karabak & Ince, 2009). Traditional Principal Component Analysis (PCA) is one of the most frequently used feature extraction methods. It depends on extracting the axes on which data exhibit the maximum randomness (Jollife, 1986). Cluster analysis is a normally applied data mining method to scrutinize the relationships among attributes, samples and the relationships among attributes and samples. Hierarchical clustering tree (HCT) (Eisen, Spellman, Brown, & Botstein, 1998) and k-means (Tavazoie, Hughes, Campbell, Cho, & Church, 1999) are the two most well-known clustering techniques used to eliminate the features from the medical data’s. Alternatively, the rough sets provide a proficient method of managing uncertainties and can be utilized for tasks such as data dependency study, feature identification, dimensionality reduction, and pattern categorization. Rough set theory (Pawlak, 1991; Polkowski, 2003) is a reasonably fresh intelligent method for managing ambiguity that is employed to find out data dependencies, to review the implication of attributes, detecting patterns in data, and to decrease redundancies.