In this research a new hybrid prediction algorithm for breast cancer has been made from a breast cancer data set. Many approaches are available in diagnosing the medical diseases like geneticalgorithm, ant colony opt...
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In this research a new hybrid prediction algorithm for breast cancer has been made from a breast cancer data set. Many approaches are available in diagnosing the medical diseases like geneticalgorithm, ant colony optimization, particle swarm optimization, cuckoo search algorithm, etc., The proposed algorithm uses a ReliefF attribute reduction with entropy based genetic algorithm for breast cancer detection. The hybrid combination of these techniques is used to handle the dataset with high dimension and uncertainties. The data are obtained from the Wisconsin breast cancer dataset;these data have been categorized based on different properties. The performance of the proposed method is evaluated and the results are compared with other well known feature selection methods. The obtained result shows that the proposed method has a remarkable ability to generate reduced-size subset of salient features while yielding significant classification accuracy for large datasets.
Functional Magnetic Resonance imaging (fMRI) provides sequence of 3D images which contains large number of voxels as information. There are many statistical methods evolved in last few years to analyze this informatio...
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ISBN:
(纸本)9781538663738
Functional Magnetic Resonance imaging (fMRI) provides sequence of 3D images which contains large number of voxels as information. There are many statistical methods evolved in last few years to analyze this information. Main concern of all these techniques is huge dimensions of the data produced by these images. This paper proposes an efficient hybrid method for feature selection and classification. This method combine entropy based genetic algorithm (EGA) with Linear Collaborative Discriminant Regression Classification (LCDRC) to form feature based classification method. entropy based genetic algorithm is applied to find maximum significance between the input and output and also it radically reduces the redundancy within the input features. Experiments' using Star-Plus dataset to classify fMRI images shows that EGA-LCDRC reduces up to 60% features and produces 96.73% accuracy.
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