Breast Cancer Diagnosis Based on K-Means and SVM

Transcription

1 Breast Cancer Diagnosis Based on K-Means and SVM Mengyao Shi UNC STOR May 4, 2018 Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

2 Background Cancer is a major health problem in the United States. Diagnosing the tumors has become one of the trending issues in the medical field. Traditionally, breast cancer was predicted based on the mammography by radiologists and physicians. But it is difficult for them to predict the tumor types. The traditional breast cancer diagnosis was transferred into a classification problem in the data mining domain. Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

3 Background The amount of available data (both features and records) has increased dramatically. The redundant information leads to a larger computation time for tedious calculation and can disturb the model. Methodologies for recognizing tumor patterns and extracting the necessary information for breast cancer diagnosis need to be studied. Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

4 Literature Review SVM generated a more accurate result (97.2 %) than decision tree based on the Breast Cancer Wisconsin Dataset(WDBC) (Bennett & Blue, 1998). In the research by Akay (2009), SVM provided about 99% based on Breast Cancer Wisconsin Dataset(WDBC), using a genetic algorithm to select variables Polat and Gunes (Polat & Gunes, 2007) proposed least square support vector machine (LS-SVM) based on the same data set with accuracy of 98.53%. Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

5 Literature Review Feature selection is mainly based on the performance of different feature combination. Prasad(2010) used GA, ACO, PSO to select variables for SVM. The PSO-SVM showed the best results with 100% accuracy while GA-SVM provided 98.95% accuracy based on the WDBC. Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

6 Literature Review A generalized representation of patterns, called symbolic objects, was defined(chidananda Gowda & Diday, 1991; Jain et al., 1999). K-Means was a good method for recognizing a hidden pattern from the data set but was not often utilized for predicting and classification problems. Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

7 Method: Data Description 565 observations (tumors) 30 features in 10 categories tumor type Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

8 Method: Feature Extraction and Selection step1: Find new symbolic tumors through K-Means Method use K-Means for pattern recognition min µ K k=1 i S k X i µ k 2 Inheriting the idea of symbolic objects, the K-means algorithm is used for clustering tumors based on similar malignant and benign tumor features respectively. Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

9 Method: Feature Extraction and Selection step1: Find new symbolic tumors through K-Means Method how to determine K d avg = K = argmin K θ = argmin K d avg d min K k=1 i S k F j=1 (X i j X µ k j ) 2 d min = min F (X µm j j=1 N X µ k j ) 2 k m Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

10 Method: Feature Extraction and Selection step1: Find new symbolic tumors through K-Means Method Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

11 Method: Feature Extraction and Selection step2: Reconstruct New Features measure the similarity of the original data point and the symbolic tumors membership equation f k (Xj i 1 X µ k j Xj i ) = max X µ k min(x n j Xj n j ) <= X j i <= max(xj n), n S k 0 otherwise (1) p k = 1 F F j=1 f k (X i j ), 1 <= k <= K m + K b Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

12 Method: Classifier SVM maximize α [ n α i 1 2 i=1 n α i α j y i y j K(x i, x j )] i,j=1 n subject to α i y i = 0, 0 <= α i <= L i=1 Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

13 Experimental Result Accuracy = TP+TN TP+TN+FP+FN The diagnosis accuracy is maintained at 97.38%. Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

14 Experimental Result Compare SVM and K-SVM Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

15 Experimental Result Compare different variable selection combined with SVM Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

16 Summary Clustering is used to extract the symbolic tumor objects to represent tumor clusters. These patterns are reconstructed as the new abstract tumor features for the training phase. According to the result, the K- SVM reduces the computation time significantly without losing diagnosis accuracy. Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

17 Reference Elmore, J. G., Wells, C. K., Lee, C. H., Howard, D. H., & Feinstein, A. R. (1994).Variability in radiologists interpretations of mammograms. New England Journal of Medicine, 331, Bennett, K. P., & Blue, J. A. (1998). A support vector machine approach to decision trees. In Proceedings of IEEE world congress on computational intelligence (pp ). Anchorage, AK: IEE. Akay, M. F. (2009). Support vector machines combined with feature selection for breast cancer diagnosis. Expert Systems with Applications, 36, Polat, K., & Gunes, S. (2007). Breast cancer diagnosis using least square support vector machine. Digital Signal Processing, 17, Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

18 Reference Prasad, Y., Biswas, K., & Jain, C. (2010). Svm classifier based feature selection using ga, aco and pso for sirna design. In Proceedings of the first international conference on advances in swarm intelligence (pp ). Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys (CSUR), 31, Chidananda Gowda, K., & Diday, E. (1991). Symbolic clustering using a new dissimilarity measure. Pattern Recognition, 24, Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19

19 The End Mengyao Shi (UNC STOR) Breast Cancer Diagnosis Based on K-Means and SVM May 4, / 19