Brain Magnetic Resonance Spectroscopy Classifiers
During the last decade, the Magnetic Resonance Spectroscopy modality has become an integrant part of the diagnostic routine. However, the visual interpretation of these spectra is difficult and few clinicians are trained to use the technique. In this study, sixty-eight spectra obtained from twenty-two multi-voxel spectroscopies were classified using three well-known classification algorithms: K-Nearest Neighbors (KNN), Decision Trees and Naïve Bayes. The best results were obtained using NaïveBayes that presented an average balanced accuracy rate around 75%, although K-Nearest Neighbors presented very good results in some situations. The obtained results lead us to conclude that it is possible to classify magnetic resonance spectra with data mining techniques for further integration in a Clinical Decision Support System which may help in the diagnosis of new cases.
KeywordsFeature Selection Magnetic Resonance Spectroscopy Linear Discriminant Analysis Clinical Decision Support System Data Mining Technique
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- 1.Bushberg, J.T., Seibert, J.A., Leidholdt, E.M., Boone, J.M.: The Essential Physics of Medical Imaging, 2nd edn. Lippincott Williams & Wilkins, Philadelphia (2002)Google Scholar
- 8.Siddall, et al.: Magnetic Resonance Spectroscopy Detects Biochemical Changes in the Brain Associated with Chronic Low Back Pain: A Preliminary Report. Anesthesia and Analgesia 102, 1164-1168 (2006)Google Scholar
- 11.Siemens, MR Spectroscopy Operator Manual: Version syngo MR 2002B. Erlangen (2002)Google Scholar
- 12.Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2000)Google Scholar
- 14.Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-Interscience, Hoboken (2000)Google Scholar
- 15.Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, Heidelberg (2003)Google Scholar
- 16.Berry, M.J.A., Linoff, G.S.: Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Wiley Computer Publishing, Indianapolis (2004)Google Scholar
- 17.Kantardzic, M.: Data Mining: Concepts, Models, Methods and Algorithms. Wiley-IEEE Press (2002)Google Scholar
- 18.An, A.: Classification Methods. In: Wang, J. (ed.) Encyclopedia of Data Warehousing and Mining, pp. 144–149. Idea Group Publishing, Hershey (2006)Google Scholar
- 20.Kohavi, R.: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: IJCAI (International Joint Conferences on Artificial Intelligence), Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Québec, Canada, August 20-25, Morgan Kaufmann, San Francisco (1995)Google Scholar