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Prediction of Sickle Cell Anemia Patient’s Response to Hydroxyurea Treatment Using ARTMAP Network

Prediction of Sickle Cell Anemia Patient’s Response to Hydroxyurea Treatment Using ARTMAP Network. Hongyu Xu, Faramarz Valafar, Marko Vuskovic Department of Computer Science San Diego State University San Diego, CA 92182-7720 METMBS 2003 Las Vegas, June 24, 2003.

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Prediction of Sickle Cell Anemia Patient’s Response to Hydroxyurea Treatment Using ARTMAP Network

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  1. Prediction of Sickle Cell Anemia Patient’s Response to Hydroxyurea Treatment Using ARTMAP Network Hongyu Xu, Faramarz Valafar, Marko Vuskovic Department of Computer Science San Diego State University San Diego, CA 92182-7720 METMBS 2003 Las Vegas, June 24, 2003 The full paper and these slides are available at: http://medusa.sdsu.edu/Robotics/Neuromuscular Control/Neuromuscular.htm

  2. Contents • Sickle cell anemia • Data and data preprocessing • Linear dependency of features • Feature selection • Data labeling • MART clustering algorithm • MART classification algorithm • Results • Conclusion

  3. Sickle Cell Anemia • Sickle cell anima is a genetic disorder, caused by single point mutation in the beta globin gene that changes from CCTGAGG to CCTGTGG. • The molecules of sickle cell hemoglobin adhere to each other and distort red blood cells (RBC) into sickle shape . They stick in narrow blood vessels, blocking the flow of blood. • Sickle cell patients experience severe painful crises.Many sickle cell patients die before the age of 20. • In the United States, about 1 in 500 African Americans develops sickle cell anima [5]. In Africa, about 1 in 100 individuals develop the disease. • In 1983, a drug called hydroxyurea (HU) was first used on sickle cell patients. • The patients who responded to HU treatment positively experienced less pain and their life span were prolonged, but HU can also be quite toxic.

  4. Patient Features Note: The data used in this research is obtained from University of Georgia, Structural Genomics Group. Dr. Homayoun Valafar was responsible for the data collection and preprocessing.

  5. Excerpt from patient’s data 1.0e+004 *

  6. Data Preprocessing • Normalization • Log transformation • Treatment of incomplete features

  7. Patient Data (after log transform)

  8. Linear Dependency of Features

  9. Linear Dependency of Features (Cont.)

  10. SBAN SBEN SCAM SSEN

  11. Before removal: After removal:

  12. Feature Selection

  13. Feature Selection (Cont.)

  14. Data Labeling

  15. Data Labeling (Cont.)

  16. Representation of Patient’s Data in Reduced Feature Space Double rule

  17. Representation of Patient’s Data in Reduced Feature Space (Cont.) 15% rule

  18. Approaches in Pattern Recognition Pattern recognition Neural networks Bayes’ Classifier Feedforward Recurrent Probability density estimation Single layer Perceptrons Multilayer Perceptrons Radial Basis Function ART Maximum liklihood Parzen window Mixture model Basis functions MART Bayesian inference K-nearest neighbor K-mean SOM Mixture model

  19. ART NetworksGrossberg, 1976 Supervised ART Learning Unsupervised ART Learning ART1, ART2 Carpenter & Grossberg, 1987 Fuzzy ART Carpenter, Grossberg, etal,1991 ARTMAP Carpenter, Grossberg, etal,1991 Fuzzy ARTMAP Carpenter, Grossberg, etal,1991 Gaussian ARTMAP Williamson,1992 Simplified ARTMAP Kasuba, 1993 Simplified ART Baraldi and Alpaydin, 1998 Mahalanobis distance based ARTMAP Vuskovic & Du, 2001 Vuskovic, Xu & Du, 2002

  20. MART clustering Algorithm

  21. MART clustering Algorithm (Cont.)

  22. MART Functions

  23. MART Classification Algorithm The trained network is a Gaussian mixture model. Each class maps to one or more clusters. The class probability is proportional to the sum of posterior probabilities of individual clusters of the same class. The prediction is class that yields the maximum class probability. Prior probability of cluster j Posterior probability Class conditional pdf of x given cluster j

  24. Results

  25. Conclusion • MART has shown superior performance in various benchmarks, which has inspired us to apply MART to sickle cell anemia patients data. • MART achieved 96.82% accuracy for predicting responders to HU treatment and give 92.59% global accuracy. • Removal of linear dependency of features has improved the numerical stability of the algorithms. • Reduction of the feature space from 23 to only 3 features has considerably improved the performance (decreased the numerical complexity and even increased the accuracy) • In the future we plan to explore other labeling methods. • We also plan to investigate more data preprocessing methods, which include both linear and nonlinear transformations.

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