A hybrid Genetic K-Means Algorithm forFeatures Selection to Classify Medical Datasets
journal of kerbala university,
2013, Volume 9, Issue 3, Pages 139-149
AbstractRelevant features selection is become primary preprocessing step for building almost intelligence machine learning systems. Feature Selection (FS) is more and more important in many applications such as patterns recognition, medical technologies, data mining environments and others. The main objective of FS is to choice the important features among multi set in order to building effective machine learning models such as pattern analysis model by cancelling irrelevant or redundant attributes. An addition to that, there is a fact that the efficiency of the desired system is very sensitive to choose of the features that effect on classification or any analysis procedure of small or high dimensional datasets. Furthermore, the analysis of medical datasets has become growing claiming problem, due to huge datasets that cause time consuming and uses additional computational effort, which may not be suitable for many applications.
This work attempts to introduce a hybrid genetic k-means of feature selection algorithm for multi medical diseases datasets. The proposed algorithm uses a genetic algorithm combine with k-means algorithm as a powerful tool to select the relevant features from different large medical datasets of Mirjan hospital diabetes, heart and breast cancer diseases which play the important role in maximum the classification accuracy and efficiency of the system. Experimental results show the efficiency of the proposed system for the used datasets and satisfy maximum classification accuracy performance compared with others states
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