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Carpal Tunnel Syndrome automatic classification: electromyography vs. ultrasound imaging

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Abstract

We study automatic classification for the diagnosis of the Carpal Tunnel Syndrome (CTS), a disease frequently observed in occupational medicine. We apply different classification techniques to two real-life medical data sets related to a group of patients reporting the typical symptoms of this syndrome. We are particularly interested in the performance of “Box-Clustering” (BC), a method that is able to favor readability and interpretation of the results by medical doctors, thanks to its “box-type” output which naturally configures as a medical report. Preliminary results of a basic implementation of BC applied to different data sets already exist in the literature, and here we add more. In particular, in this paper, we apply a recently developed (and specialized) implementation of BC, and we test it for the first time on real-life medical data related to the CTS. Our purpose is to evaluate the performance of BC for automatic diagnosis, as well as, gain in explanation capability and interpretability. This is, in fact, a crucial aspect in medical applications that generally represents a limit for other well-known and powerful classification techniques.

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Notes

  1. We thank Doctor Gioia for the opportunity he gave us of applying BC to real-life data, and for the useful discussions we had with him at the beginning of this work.

  2. The two examinations were performed independently by two different doctors working in different sites in Italy, one at the Department of Neurophysiopathology of the “San Salvatore” Hospital in the city of L’Aquila, and the other at the INAIL-Abruzzi Regional Polydiagnostic Center.

  3. It was performed by a third medical doctor under the protocol that he was neither informed about the diagnosis based on the other examination, nor about the specific clinics of the subject under study.

  4. It can be seen as a product of Boolean variables and of complemented Boolean variables.

  5. Obviously, every pattern in a positive (or negative) theory takes the value \(0\) in every negative (resp. positive) point.

  6. This definition can be easily extended to non-real but totally ordered variables.

  7. http://www.ics.uci.edu/mlearn/MLRepository.html.

  8. We point out that these experiments are a selection from a wider experimental work performed on the same BC implementation applied in this paper Spinelli (2014a).

References

  • Alexe G, Alexe S, Axelrod DE, Bonates TO, Lozina I, Reiss M, Hammer PL (2006) Breast cancer prognosis by combinatorial analysis of gene expression data. Breast Cancer Res 8:1–20

    Article  Google Scholar 

  • Alexe S, Blackstone EH, Hammer PL, Ishwaran H, Lauer MS, Snader CEP (2003) Coronary risk prediction by logical analysis of data. Ann Oper Res 119:15–42

    Article  Google Scholar 

  • Alexe G, Hammer PL, Kogan PL (2002) Comprehensive vs. comprehensible classifiers in logical analysis of data, RUTCOR Research Report, RRR 9/2002

  • Anthony M, Ratsaby J (2012) Using boxes and proximity to classify data into several categories. RUTCOR Research Reports, RRR 7/2012

  • Arlot S, Celisse A (2010) A survey of cross-validation procedures for model selection. Stat Surv 4:40–79

    Article  Google Scholar 

  • Barakat N, Diederich J (2005) Eclectic rule-extraction from support vector machines. Int J Comput Intell 2:59–62

    Google Scholar 

  • Beekman R, Visser LH (2003) Sonography in the diagnosis of carpal tunnel syndrome: a critical review of the literature. Muscle Nerve 27:26–33

    Article  Google Scholar 

  • Bonates T, Hammer PL (2006) Logical analysis of data: from combinatorial optimization to medical applications. Ann Oper Res 148:203–225

    Article  Google Scholar 

  • Boros E, Hammer PL, Ibaraki T, Kogan A, Mayoraz E, Muchnik I (2000) An implementation of logical analysis of data. IEEE Trans Knowl Data Eng 12:292–306

    Article  Google Scholar 

  • Boros E, Ibaraki T, Shi L, Yagiura M (2000) Generating all good patterns in polynomial expected time. In: Lecture at the 6th International Symposium on Artificial Intelligence and Mathematics. Ft. Lauderdale, Florida

  • Boros E, Hammer PL, Ibaraki T, Kogan A (1997) A logical analysis of numerical data. Math Program 79:163–190

    Google Scholar 

  • Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. Chapman & Hall, London

    Google Scholar 

  • Cantatore FP, Dell’Accio F, Lapadula G (1997) Carpal Tunnel Syndrome: a review. Clin Rheumatol 16:596–603

    Article  Google Scholar 

  • Carrizosa E, Martin-Barragan B, Romero Morales D (2010) Binarized support vector machines. INFORMS J Comput 22:154–167

    Google Scholar 

  • Carrizosa E, Martin-Barragan B, Romero Morales D (2011) Detecting relevant variables and interactions in supervised classification. Eur J Oper Res 213:260–269

    Article  Google Scholar 

  • Crama Y, Hammer PL, Ibaraki T (1988) Cause–effect relationships and partially defined Boolean functions. Ann Oper Res 16:299–325

    Article  Google Scholar 

  • Du D, Pardalos PM, Wang J (2000) Discrete mathematical problems with medical applications, DIMACS Series, vol 55. American Mathematical Society

  • Eckstein J, Hammer PL, Liu Y, Nediak M, Simeone B (2002) The maximum box problem and its application to data analysis. Comput Optim Appl 23:285–298

    Article  Google Scholar 

  • Ekin O, Hammer PL, Kogan A (1998) Convexity and logical analysis of data. RUTCOR Research Report, RRR 5/1998

  • Felici G, Spinelli V (2009) Genetic procedure for over-training control in logic mining. Istituto di Analisi dei Sistemi ed Informatica-IASI/CNR. Technical Report 19/2009

  • Felici G, Simeone B, Spinelli V (2008) Special issue on data mining. In: Sharda R, Voß S (eds) Classification techniques and error control in logic mining., Annals of Information Systems SeriesSpringer, New York

  • Felici G, Sun F-S, Truemper K (2006) Data mining and knowledge discovery approaches based on rule induction techniques. In: Felici G, Trintaphyllou E (eds) Learning logic formulas and related error distributions. Springer Science, New York

    Google Scholar 

  • Felici G, Truemper K (2001) A MINSAT approach for learning in logic domains. INFORMS J Comput 13:1–17

    Article  Google Scholar 

  • Hall M, Eibe F, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor 11:10–18

    Article  Google Scholar 

  • Hammer PL, Kogan A, Simeone B, Szedmák S (2001) Pareto-optimal patterns in logical analysis of data. RUTCOR Research Report, RRR 7/2001

  • Hammer PL, Liu Y, Simeone B, Szedmák S (2004) Saturated systems of homogeneous boxes and the logical analysis of numerical data. Discret Appl Math 144:103–109

    Article  Google Scholar 

  • Huang J, Ling CX (2005) Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng 17:299–310

    Article  Google Scholar 

  • Isolani L, Bonfiglioli R, Raffi GB, Violante FS (2002) Different case definitions to describe the prevalence of occupational carpal tunnel syndrome in meat industry workers. Int Arch Occup Environ Health 75:229–234

    Article  Google Scholar 

  • Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the Fourteenth International Joint Conference on artificial intelligence, IJCAI 95. Montrèal, (Quèbec, Canada

  • Lee D, van Holsbeeck MT, Janevski PK, Ganos DL, Ditmars DM, Darian VB (1999) Diagnosis of Carpal Tunnel Syndrome: ultrasound versus electromyography. Radiol Clin N Am 37:859–872

    Article  Google Scholar 

  • Leffler CT, Gozani SN, Nguyen ZQ, Cros D (2000) An automated electrodiagnostic technique for detection of carpal tunnel syndrome. Neurol Clin Neurophysiol 3:2–10

    Article  Google Scholar 

  • Mitchell T (1997) Machine learning. McGraw-Hill, New York

    Google Scholar 

  • Mugan J, Truemper K (2008) Discretization of rational data in mathematical methods for knowledge discovery and data mining. In: Felici G, Vercellis C (eds) Chapter I, Hershey: information science reference

  • Pacek CA, Tang J, Goitz RJ, Kaufmann RA, Li ZM (2010) Morphological analysis of the carpal tunnel. Hand 5:77–81

    Article  Google Scholar 

  • Papanicolau GD, McCabe SJ, Firrell J (1987) The prevalence and characteristics of nerve compression symptoms in the general population. J Hand Surg 12:712–717

    Article  Google Scholar 

  • Pardalos PM, Hansen P (2008) Data mining and mathematical programming. American Mathematical Society, Providence

    Google Scholar 

  • Pardalos PM, Romeijn E (2009) Handbook of optimization in medicine. Springer, New York

    Google Scholar 

  • Sakai S, Togasaki M, Yamazaki K (2003) A note on greedy algorithms for the maximum independent set problem. Discret Appl Math 126:313–322

    Article  Google Scholar 

  • Simeone B, Boros E, Ricca F, Spinelli V (2011) Incompatibility graphs in data mining, Department of Statistical Sciences, Sapienza, University of Rome, Technical Report 10/2011 (submitted to Journal of Graph Theory)

  • Simeone B, Spinelli V (2007) The optimization problem framework for box clustering approach in logic mining. In: Proceedings of Euro XXII-22nd European Conference on Operational Research, Prague

  • Simeone B, Felici G, Spinelli V (2007) A graph coloring approach for box clustering techniques in logic mining. In: Proceedings of Euro XXII-22nd European Conference on Operational Research, Prague

  • Spinelli V (2009) Logic mining, box-clustering, and graphs. PhD Thesis, University of Rome, Sapienza

  • Spinelli V (2014) Classification and pruning in logic mining. Adv Data Anal Classif (submitted)

  • Spinelli V (2014) Problems and algorithms in Box-Clustering Adv Data Anal Classif (submitted)

  • Wu S, Flach P (2005) A scored AUC metric for classifier evaluation and selection. In: Second Workshop on ROC Analysis in ML, Bonn, Germany

  • Zweig MH, Campbell G (1993) Receiver-Operating Characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39:561–577

    Google Scholar 

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Authors and Affiliations

  1. Dip. di Ingegneria e Scienze dell’Informazione e Matematica, Università degli Studi dell’Aquila, L’Aquila, Italy

    Maurizio Maravalle

  2. Dip. di Scienze Statistiche, Sapienza Università di Roma, Rome, Italy

    Federica Ricca & Bruno Simeone

  3. Istituto Nazionale di Statistica (Istat), Rome, Italy

    Vincenzo Spinelli

Authors
  1. Maurizio Maravalle
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  2. Federica Ricca
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  3. Bruno Simeone
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  4. Vincenzo Spinelli
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Correspondence to Federica Ricca.

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Maravalle, M., Ricca, F., Simeone, B. et al. Carpal Tunnel Syndrome automatic classification: electromyography vs. ultrasound imaging. TOP 23, 100–123 (2015). https://doi.org/10.1007/s11750-014-0325-0

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