Abstract
This paper describes a process of rule-extraction from a multi-centre brain tumour database consisting of nuclear magnetic resonance spectroscopic signals. The expert diagnosis of human brain tumours can benefit from computer-aided assistance, which has to be readily interpretable by clinicians. Interpretation can be achieved through rule extraction, which is here performed using the LR-FIR algorithm, a method based on fuzzy logic. The experimental results of the classification of three groups of tumours indicate in this study that just three spectral frequencies, out of the 195 from a range pre-selected by experts, are enough to represent, in a simple and intuitive manner, most of the knowledge required to discriminate these groups.
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Garg, A.X., Adhikari, N.K.J., McDonald, H., Rosas-Arellano, M.P., Devereaux, P.J., Beyene, J., Sam, J., Haynes, R.B.: Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes: A Systematic Review. J. Amer. Med. Assoc. 293, 1223–1238 (2005)
Tung, W.L., Quek, C.: GenSo-FDSS: a Neural-Fuzzy Decision Support System for Pediatric ALL Cancer Subtype Identification Using Gene Expression Data. Artif. Intell. Med. 33, 61–88 (2005)
Mitra, S.: Computational Intelligence in Bioinformatics. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 134–152. Springer, Heidelberg (2005)
Vellido, A., Lisboa, P.J.G.: Neural Networks and Other Machine Learning Methods in Cancer Research. In: Sandoval, F., Gonzalez Prieto, A., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 964–971. Springer, Heidelberg (2007)
Futschik, M.E., Reeve, A., Kasabov, N.: Evolving Connectionist Systems for Knowledge Discovery from Gene Expression Data of Cancer Tissue. Artif. Intell. Med. 28, 165–189 (2003)
Chen, Z., Lia, J., Wei, L.: A Multiple Kernel Support Vector Machine Scheme for Feature Selection and Rule Extraction from Gene Expression Data of Cancer Tissue. Artif. Intell. Med. 41, 161–175 (2007)
Peña-Reyes, C.A., Sipper, M.: A Fuzzy-Genetic Approach to Breast Cancer Diagnosis. Artif. Intell. Med. 17, 131–155 (1999)
Takahashi, H., Masuda, K., Ando, T., Kobayashi, T., Honda, H.: Prognostic Predictor with Multiple Fuzzy Neural Models Using Expression Profiles from DNA Microarray for Metastases of Breast Cancer. J. Biosci. Bioeng. 98, 193–199 (2004)
Hassanien, A.E.: Fuzzy Rough Sets Hybrid Scheme for Breast Cancer Detection. Image Vision Comput. 25, 172–183 (2007)
Artificial Intelligence Decision Tools for Tumour diagnosis (AIDTumour) research project, http://www.lsi.upc.edu/~websoco/AIDTumour
Julià-Sapé, M., et al.: A Multi-Centre, Web-Accessible and Quality Control-Checked Database of in Vivo MR Spectra of Brain Tumour Patients. Magn. Reson. Mater. Phy. MAGMA 19, 22–33 (2006)
Castro, F., Nebot, A.: Un Algoritmo para la Extracción Automática de Reglas Lógicas a partir de Modelos FIR. Technical Report, Universitat Politècnica de Catalunya LSI-07-7-R (2007)
Nebot, A., Cellier, F.E., Vallverdú, M.: Mixed Quantitative:Qualitative Modeling and Simulation of the Cardiovascular System. Comput. Meth. Prog. Bio. 55, 127–155 (1998)
Escobet, A., Nebot, A., Cellier, F.E.: Visual-FIR: A Tool for Model Identification and Prediction of Dynamical Complex Systems. Simul. Model. Pract. Th. 16, 76–92 (2008)
Vellido, A., Biganzoli, E., Lisboa, P.J.G.: Machine Learning in Cancer Research: Implications for Personalised Medicine. In: 16th European Symposium on Artificial Neural Networks (ESANN 2008). d-Side pub, Evere, Belgium (2008) (in press)
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Nebot, À., Castro, F., Vellido, A., Julià-Sapé, M., Arús, C. (2008). Rule-Based Assistance to Brain Tumour Diagnosis Using LR-FIR. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85565-1_22
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DOI: https://doi.org/10.1007/978-3-540-85565-1_22
Publisher Name: Springer, Berlin, Heidelberg
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