An efficient framework for heart disease classification using feature extraction and feature selection technique in data mining
In the classification of the heart disease data set a high dimensional data set is used in the pre processing stage of data mining process. This raw dataset consist of redundant and inconsistent data thereby increasing the search space and storage of the data. To achieve the classification accuracy we need to remove the redundant and the irrelevant data present. The dimensionality reduction technique is used to compress the high dimensional data to lower dimensional data with some constraints. A framework is integrated for the easy prediction of the heart disease. The framework is created by using the principal component analysis (PCA) to extract the features and mathematical model is computed to select the relevant features using the relevant constraint. This proposed work helps in improving the efficiency, accuracy and speed of the process. This can be applied in the applications such as information retrieval, image processing and pattern matching.
2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS)