Decision tree classifiers for automated medical diagnosis

Neural Computing and Applications

Volumn 23, Issue 7-8, 2013, Pages 2387-2403

  Azar, Ahmad Taher ^a   El Metwally, Shereen M ^b  

^a MISR UNIVERSITY FOR SCIENCE AND TECHNOLOGY   (Egypt)

^b CAIRO UNIVERSITY   (Egypt)

Author keywords

Boosted decision tree; Computer aided diagnosis (CAD); Decision support systems (DSS); Decision tree classification; Decision tree forest; k fold cross validation; Single decision tree

Indexed keywords

BOOSTED DECISION TREES; DECISION SUPPORT SYSTEM (DSS); DECISION TREE CLASSIFICATION; K FOLD CROSS VALIDATIONS; SINGLE DECISION;

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; DATA MINING; DECISION SUPPORT SYSTEMS; DECISION TREES; DISEASES; FORESTRY; STATISTICAL TESTS;

DECISION MAKING;

ARTIFICIAL INTELLIGENCE; CLASSIFICATION; DECISION MAKING; DISEASES; FORESTRY; STATISTICAL ANALYSIS;

EID: 84887478929     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-012-1196-7     Document Type: Article

References (89)

1. Alsabti K, Ranka S, Singh V (1998) CLOUDS: a decision tree classifier for large datasets. In: Proceedings of the fourth international conference on knowledge discovery and data mining (KDD-98), August 27-31. AAAI Press, New York City, NY, USA, pp 2-8.
2. Amit Y, Geman D (1997) Shape quantization and recognition with randomized trees. Neural Comput 9(7): 1545-1588.
3. Ankerst M, Elsen C, Ester M, Kriegel HP (1999) Visual classification: an interactive approach to decision tree construction. In: Proceedings of international conference on knowledge discovery and data mining (KDD '99), San Diego, CA, USA.
4. Arditi D, Pulket T (2005) Predicting the outcome of construction litigation using boosted decision trees. J Comput Civil Eng 19(4): 387-393.
5. Balakumaran T, Vennila ILA, Shankar GC (2010) Microcalcification detection in digital mammograms using novel filter bank. Procedia Comput Sci 2: 272-282.
6. Bick U, Diekmann F (2007) Digital mammography: what do we and what don't we know? Eur Radiol 17(8): 1931-1942.
7. Boyle P, Levin B (2008) World cancer report 2008. International Agency for Research on Cancer, Lyon.
8. Bradford JP, Kunz C, Kohavi R et al (1998) Pruning decision trees with misclassification costs. In: Proceedings of the 10th European conference on machine learning, Chemnitz, Germany, pp 131-136, April 21-23, 1998.
9. Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth & Brooks, CA.
10. Breiman L (1994) Bagging predictors. Technical report 421. Department of Statistics, University of California, Berkeley.
11. Breiman L (2001) Random forests. Mach Learn 45(1): 5-32.
12. Brown DE (2008) Introduction to data mining for medical informatics. Clin Lab Med 28(1): 9-35.
13. Burrell HC, Sibbering DM, Wilson AR et al (1996) Screening Interval breast cancers: mammographic features and prognostic factors. Radiology 199(3): 811-817.
14. Burrell HC, Pinder SE, Wilson AR et al (1996) The positive predictive value of mammographic signs: a review of 425 non-palpable breast lesions. Clin Radiol 51(4): 277-281.
15. Clark LA, Pregibon D (1992) Tree-based models. In: Chambers JM, Hastie TJ (eds) Statistical models (chap 9). S. Chapman & Hall, New York, pp 377-420.
16. Christoyianni I, Koutras A, Dermatas E, Kokkinakis G (2002) Computer aided diagnosis of breast cancer in digitized mammograms. Comput Med Imaging Graph 26(5): 309-319.
17. Cummings MP, Segal MR (2004) Few amino acid positions in rpoB are associated with most of the rifampin resistance in Mycobacterium tuberculosis. BMC Bioinform 5: 137-143.
18. De'ath G (2007) Boosted trees for ecological modeling and prediction. Ecology 88(1): 243-251.
19. De'ath G, Fabricius KE (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81(11): 3178-3192.
20. Delen D, Walker G, Kadam A (2005) Predicting breast cancer survivability: a comparison of three data mining methods. Artif Intell Med 34(2): 113-127.
21. Dershaw DD (2006) Status of mammography after the digital mammography imaging screening trial: digital versus film. Breast J 12(2): 99-102.
22. DeSantis C, Siegel R, Bandi P, Jemal A (2011) Breast cancer statistics. CA Cancer J Clin 61(6): 409-418.
23. Diamantidis NA, Karlis D, Giakoumakis EA (2000) Unsupervised stratification of cross-validation for accuracy estimation. Artif Intell 116(1-2): 1-16.
24. Dietterich TG (1990) Machine learning. Annu Rev Comput Sci 4(1): 255-306.
25. Doi K, MacMahon H, Katsuragawa S et al (1999) Computer-aided diagnosis in radiology: potential and pitfalls. Eur J Radiol 31(2): 97-109.
26. Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31(4-5): 198-211.
27. Elatar I (2002) Cancer registration, NCI Egypt 2001. National Cancer Institute, Cairo. http://www. nci. edu. eg/Journal/nci2001%20. pdf, accessed 26 May 2012.
28. Endo A, Shibata T, Tanaka H (2008) Comparison of seven algorithms to predict breast cancer survival. Biomed Soft Comput Hum Sci 13(2): 11-16.
29. Fan CY, Changb PC, Linb JJ, Hsieh JC (2011) A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification. Appl Soft Comput 11(1): 632-644.
30. Ferri U, Flach PA, Hernandez-Orallo J (2003) Improving the AUC of probabilistic estimation trees. In: Lecture notes in artificial intelligence, vol 2837, pp 121-132.
31. Francois D, Rossi F, Wertz V, Verleysen M (2007) Resampling methods for parameter-free and robust feature selection with mutual information. Neurocomputing 70(7-9): 1276-1288.
32. Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Proceedings of the 13th international conference on artificial intelligence: machine learning. International Machine Learning Society, pp 148-156.
33. Freund Y, Schapire RE (1999) A short introduction to boosting. J Jpn Soc Artif Intell 14(5): 148-156.
34. Friedman JH, Kohavi R, Yun Y (1996). Lazy decision trees. In: Proceedings of the 13th national conference on artificial intelligence and eighth innovative applications of artificial intelligence conference, vol 1. AAAI Press/The MIT Press, AAAI 96, IAAI 96, August 4-8, 1996, pp 717-724.
35. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Statist 29(5): 1189-1232.
36. Fulton T, Kasif S, Salzberg S, Waltz D (1996) Local induction of decision trees: towards interactive data mining. In: Proceedings of the second international conference on knowledge discovery and data mining, Portland, OR, USA, pp 14-19.
37. Garofalakis M, Hyun D, Rastogi R, Shim K (2000). Efficient algorithms for constructing decision trees with constraints. In: Proceedings of the 6th ACM SIGKDD international conference on knowledge discovery and data mining, Boston, MA, USA, pp 335-339.
38. Hambly NM, McNicholas MM, Phelan N, Hargaden GC, O'Doherty A, Flanagan FL (2009) Comparison of digital mammography and screen-film mammography in breast cancer screening: a review in the Irish breast screening program. Am J Roentgenol 193(4): 1010-1018.
39. Ho T (1995) Random decision forest. In: 3rd international conference on document analysis and recognition, Montreal, Canada, August 14-18, 1995, pp 278-282.
40. Ho T (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8): 832-844.
41. Houssami N, Given-Wilson R, Ciatto S (2009) Early detection of breast cancer: overview of the evidence on computer-aided detection in mammography screening. J Med Imaging Radiat Oncol 53(2): 171-176.
42. Ibrahim NA, Kudus A, Daud I, Abu Bakar MR (2008) Decision tree for competing risks survival probability in breast cancer study. Proc World Acad Sci Eng Technol 38: 15-19.
43. Islam SR, Aziz SM (2012) Mammography is the most effective method of breast cancer screening. Mymensingh Med J 21(2): 366-371.
44. Kallergi M (1998) Digital mammography: from theory to practice. Cancer Control 5(1): 72-79.
45. Kerekes J (2008) Receiver operating characteristic curve confidence intervals and regions. IEEE Geosci Remote Sens Lett 5(2): 251-255.
46. Kuo WJ, Chang RF, Chen DR, Lee CC (2001) Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images. Breast Cancer Res Treat 66(1): 51-57.
47. Jemal A, Murray T, Samuels A, Ghafoor A, Ward E, Thun MJ (2003) Cancer statistics. CA Cancer J Clin 53: 5-26.
48. Jerez-Aragonés JM, Gómez-Ruiz JA, Ramos-Jiménez G, Muñoz-Pérez J, Alba-Conejo E (2003) A combined neural network and decision trees model for prognosis of breast cancer relapse. Artif Intell Med 27(1): 45-63.
49. Lavrac N (1999) Selected techniques for data mining in medicine. Artif Intell Med 16(1): 3-23.
50. Laya MB, Larson EB, Taplin SH, White E (1996) Effect of estrogen replacement therapy on the specificity and sensitivity of screening mammography. J Natl Cancer Inst 88(10): 643-649.
51. Lee MY, Yang CS (2010) Entropy-based feature extraction and decision tree induction for breast cancer diagnosis with standardized thermograph images. Comput Methods Program Biomed 100(1): 269-282.
52. Lewin JM, D'Orsi CJ, Hendrick RE, Moss LJ, Isaacs PK, Karellas A, Cutter GR (2002) Clinical comparison of full-field digital mammography and screen-film mammography for detection of breast cancer. Am J Roentgenol 179(3): 671-677.
53. Li H, Giger ML, Yuan Y, Chen W, Horsch K, Lan L, Jamieson AR, Sennett CA, Jansen SA (2008) Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset. Acad Radiol 15(11): 1437-1445.
54. Lim TS, Loh WY, Shih YS (1998) An empirical comparison of decision trees and other classification methods. Technical report 979. Department of Statistics, University of Wisconsin.
55. Llora X, Garrell JM (2001) Evolution of decision trees. In: Proceedings of the 4th Catalan conference on artificial intelligence (CCIA '2001). ACIA Press.
56. Locasale JW, Cantley LC (2010) Altered metabolism in cancer. BMC Biol 88: 88.
57. Mangasarian OL, Wolberg WH (1990) Cancer diagnosis via linear programming. SIAM News 23(5): 1-18.
58. Mangasarian OL, Setiono R, Wolberg WH (1990) Pattern recognition via linear programming: theory and application to medical diagnosis. In: Coleman TF, Li Y (eds) Large-scale numerical optimization. SIAM, Philadelphia, pp 22-30.
59. Meinshausen N (2006) Quantile regression forests. J Mach Learn Res 7: 983-999.
60. Mehta M, Agrawal R, Rissanen J (1996) SLIQ: a fast scalable classifier for data mining. In: Proceedings of the 5th international conference on extending database technology, Avignon, France, March 25-29, pp 18-32.
61. McAree B, O'Donnell ME, Spence A et al (2010) Breast cancer in women under 40 years of age: a series of 57 cases from Northern Ireland. Breast 19(2): 97-104.
62. Mingers J (1989) An empirical comparison of selection measures for decision tree induction. Mach Learn 3(4): 319-342.
63. Muller S (1997) Full-field digital mammography designed as a complete system. Eur J Radiol 31(1): 25-34.
64. NHS breast screening programmes: annual review 2011. ISBN: 978-1-84463-079-0. http://www. cancerscreening. nhs. uk/breastscreen/.
65. Noble M, Bruening W, Uhl S, Schoelles K (2009) Computer-aided detection mammography for breast cancer screening: systematic review and meta-analysis. Arch Gynecol Obstet 279(6): 881-890.
66. Omar S, Khaled H, Gaafar R et al (2003) Breast cancer in Egypt: a review of disease presentation and detection strategies. East Mediterr Health J 9(3): 448-463.
67. Park SH, Goo JM, Jo CH (2004) Receiver operating characteristic (ROC) curve: practical review for radiologists. Korean J Radiol 5(1): 11-18.
68. Pryke M (2012) Effect of population-based screening on breast cancer mortality. Lancet 379(9823): 1297-1298.
69. Quinlan JR (1993) C4. 5: programs for machine learning. Morgan Kaufmann, San Mateo.
70. Quinlan JR (2003) Data mining tools See5 and C5. 0. RuleQuest Research, Austria. http://www. rulequest. com/see5-info. html.
71. Richards G, Rayward-Smith VJ, Sönksen PH, Carey S, Weng C (2001) Data mining for indicators of early mortality in a database of clinical records. Artif Intell Med 22(3): 215-231.
72. Russell S, Norvig P (2002) Artificial intelligence: a modern approach. Prentice-Hall, NJ.
73. Salzberg SL (1997) On comparing classifiers: pitfalls to avoid and a recommended approach. Data Min Knowl Discov 1(3): 317-327.
74. Shah AJ, Wang J, Yamada T, Fajardo LL (2003) Digital mammography: a review of technical development and clinical applications. Clin Breast Cancer 4(1): 63-70.
75. Shapiro S, Strax P, Venet L (1966) Evaluation of periodic breast cancer screening with mammography. Methodology and early observations. JAMA 195(9): 731-738.
76. Shanthi S, Bhaskaran VM (2011) Intuitionistic fuzzy C-means and decision tree approach for breast cancer detection and classification. Eur J Sci Res 66(3): 345-351.
77. Sherrod PH (2012) DTREG predictive modeling software. www. dtreg. com, accessed 16 Sep 2012.
78. Shiraishi A (2008) Current state of digital mammography. Breast Cancer 15(3): 194-199.
79. Sinclair N, Littenberg B, Geller B, Muss H (2011) Accuracy of screening mammography in older women. Am J Roentgenol 197(5): 1268-1273.
80. Skaane P (2009) Studies comparing screen-film mammography and full-field digital mammography in breast cancer screening: updated review. Acta Radiol 50(1): 3-14.
81. Štajduhar I, Dalbelo-Bašic′ B (2012) Uncensoring censored data for machine learning: a likelihood-based approach. Expert Syst Appl 39(1): 7226-7234.
82. Theodoridis S, Koutroumbas K (2006) Pattern recognition, 3rd edn. Academic Press, San Diego.
83. Ture M, Tokatli F, Kurt I (2009) Using Kaplan-Meier analysis together with decision tree methods (C&RT, CHAID, QUEST, C4. 5 and ID3) in determining recurrence-free survival of breast cancer patients. Expert Syst Appl 36(1): 2017-2026.
84. Tyler RM, Brady DC, Targett TE (2009) Temporal and spatial dynamics of diel-cycling hypoxia in estuarine tributaries. Estuaries Coasts 32: 123-145.
85. UCI (2012) Machine learning repository. http://archive. ics. uci. edu/ml/index. html, accessed 16 Sep 2012.
86. van Ongeval Ch (2007) Digital mammography for screening and diagnosis of breast cancer: an overview. JBR BTR 90(3): 163-166.
87. Vinnicombe S, Pinto Pereira SM, McCormack VA et al (2009) Full-field digital versus screen-film mammography: comparison within the UK breast screening program and systematic review of published data. Radiology 251(2): 347-358.
88. Wilkinson JE (2011) Effect of mammography on breast cancer mortality. Am Fam Physician 84(11): 1225-1227.
89. Yuan Q, Cai C, Xiao H et al (2007) Diagnosis of breast tumours and evaluation of prognostic risk by using machine learning approaches. Commun Comput Inf Sci 2: 1250-1260. doi: 10. 1007/978-3-540-74282.