Abstract
The application of machine learning techniques to problems in medicine are now becoming widespread, but the rational and advantages of using a particular approach is not always clear or justified. This chapter describes the application of a version of Cartesian Genetic Programming (CGP), termed Implicit Context Representation CGP, to two very different medical applications: diagnosis and monitoring of Parkinson’s disease, and the differential diagnosis of thyroid cancer. Importantly, the use of CGP brings two major benefits: one is the generation of high performing classifiers, and the second, an understanding of how the patient measurements are used to form these classifiers. The latter is typically difficult to achieve using alternative machine learning methods and also provides a unique understanding of the underlying clinical conditions.
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References
Smith, S.L., Cagnoni, S.: Genetic and Evolutionary Computation: Medical Applications. Wiley (2011)
Benamrane, N., Aribi, A., Kraoula, L.: Fuzzy neural networks and genetic algorithms for medical images interpretation. In: Geometric Modeling and Imaging–New Trends (GMAI’06), pp. 259–264. IEEE (2006)
Delibasis, K., Undrill, P.E., Cameron, G.G.: Designing texture filters with genetic algorithms: an application to medical images. Sig. Process. 57, 19–33 (1997)
Delibasis, K., Undrill, P.E., Cameron, G.G.: Designing Fourier descriptor-based geometric models for object interpretation in medical images using genetic algorithms. Comput. Vis. Image Underst. 66, 286–300 (1997)
Gudmundsson, M., El-Kwae, E.A., Kabuka, M.R.: Edge detection in medical images using a genetic algorithm. IEEE Trans. Med. Imaging 17, 469–474 (1998)
Maulik, U.: Medical image segmentation using genetic algorithms. IEEE Trans. Inf. Technol. Biomed. 13, 166–173 (2009)
Shih, F.Y., Wu, Y.-T.: Robust watermarking and compression for medical images based on genetic algorithms. Inf. Sci. 175, 200–216 (2005)
Ding, S., Li, H., Su, C., Yu, J., Jin, F.: Evolutionary artificial neural networks: a review. Artif. Intell. Rev. 39, 251–260 (2013)
Fister Jr, I., Yang, X.-S., Fister, I., Brest, J., Fister, D.: A brief review of nature-inspired algorithms for optimization.arXiv:1307.4186 (2013)
Larrañaga, P., Karshenas, H., Bielza, C., Santana, R.: A review on evolutionary algorithms in Bayesian network learning and inference tasks. Inf. Sci. 233, 109–125 (2013)
Tahmasian, M., Bettray, L.M., van Eimeren, T., Drzezga, A., Timmermann, L., Eickhoff, C.R., Eickhoff, S.B., Eggers, C.: A systematic review on the applications of resting-state fMRI in Parkinson’s disease: does dopamine replacement therapy play a role? Cortex 73, 80–105 (2015)
Zang, H., Zhang, S., Hapeshi, K.: A review of nature-inspired algorithms. J. Bionic Eng. 7, 232–237 (2010)
Langdon, W.B.: Quadratic bloat in genetic programming. In: Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation, pp. 451–458. Morgan Kaufmann Publishers Inc. (2000)
Langdon, W.B., Poli, R.: Fitness causes bloat. Soft Computing in Engineering Design and Manufacturing, pp. 13–22. Springer (1998)
Miller, J.F., Smith, S.L.: Redundancy and computational efficiency in Cartesian genetic programming. IEEE Trans. Evol. Comput. 10, 167–174 (2006)
Cai, X., Smith, S.L., Tyrrell, A.M.: Positional independence and recombination in Cartesian genetic programming. In: European Conference on Genetic Programming, pp. 351–360. Springer (2006)
Clegg, J., Walker, J.A., Miller, J.F.: A new crossover technique for cartesian genetic programming. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1580–1587. ACM (2017)
Lones, M.A., Tyrrell, A.M.: Modelling biological evolvability: implicit context and variation filtering in enzyme genetic programming. BioSystems 76, 229–238 (2004)
Smith, S.L., Leggett, S., Tyrrell, A.M.: An implicit context representation for evolving image processing filters. In: Workshops on Applications of Evolutionary Computation, pp. 407–416. Springer (2015)
Smith, S.L., Lones, M.A.: Implicit context representation Cartesian genetic programming for the assessment of visuo-spatial ability. In: IEEE Congress on Evolutionary Computation, 2009. CEC’09, pp. 1072–1078. IEEE (2009)
Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861–874 (2006)
Parkinson’s, U.: Parkinson’s prevalence in the United Kingdom. 2009. London, Parkinson’s UK. 1–13 (2012)
Bajaj, N.P., Gontu, V., Birchall, J., Patterson, J., Grosset, D.G., Lees, A.J.: Accuracy of clinical diagnosis in tremulous Parkinsonian patients: a blinded video study. J. Neurol. Neurosurg. Psychiatry 81, 1223–1228 (2010)
Das, R.: A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Syst. Appl. 37, 1568–1572 (2010)
NICE: Parkinson’s disease: national clinical guideline for diagnosis and management in primary and secondary care. Royal College of Physicians (2006)
Smith, S.L., Lones, M.A., Bedder, M., Alty, J.E., Cosgrove, J., Maguire, R.J., Pownall, M.E., Ivanoiu, D., Lyle, C., Cording, A.: Computational approaches for understanding the diagnosis and treatment of Parkinson’s disease. IET Syst. Biol. 9, 226–233 (2015)
Lones, M.A., Smith, S.L., Alty, J.E., Lacy, S.E., Possin, K.L., Jamieson, D.S., Tyrrell, A.M.: Evolving Classifiers to recognize the movement characteristics of Parkinson’s disease patients. IEEE Trans. Evol. Comput. 18, 559–576 (2014)
Lones, M.A., Alty, J.E., Lacy, S.E., Jamieson, D.S., Possin, K.L., Schuff, N., Smith, S.L.: Evolving classifiers to inform clinical assessment of Parkinson’s disease. In: IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) 2013, pp. 76–82. IEEE (2013)
Lones, M.A., Alty, J.E., Duggan-Carter, P., Turner, A.J., Jamieson, D., Smith, S.L.: Classification and characterisation of movement patterns during levodopa therapy for parkinson’s disease. In: Proceedings of the 2014 Conference Companion on Genetic and Evolutionary Computation Companion, pp. 1321–1328. ACM (2014)
Shrivastava, J.P., Mangal, K., Woike, P., Marskole, P., Gaur, R.: Role of FNAC in diagnosing thyroid neoplasms-A retrospective study. IOSR J. Dent. Med. Sci. (IOSR-JDMS) 1, 13–16
Kendall, C., Isabelle, M., Bazant-Hegemark, F., Hutchings, J., Orr, L., Babrah, J., Baker, R., Stone, N.: Vibrational spectroscopy: a clinical tool for cancer diagnostics. Analyst 134, 1029–1045 (2009)
Lones, M., Smith, S.L., Harris, A.T., High, A.S., Fisher, S.E., Smith, D.A., Kirkham, J.: Discriminating normal and cancerous thyroid cell lines using implicit context representation cartesian genetic programming. In: IEEE Congress on Evolutionary Computation (CEC) 2010, pp. 1–6. IEEE (2010)
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Smith, S.L., Lones, M.A. (2018). Medical Applications of Cartesian Genetic Programming. In: Stepney, S., Adamatzky, A. (eds) Inspired by Nature. Emergence, Complexity and Computation, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-67997-6_12
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DOI: https://doi.org/10.1007/978-3-319-67997-6_12
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