Application of C&RT, CHAID, C4.5 and WizWhy algorithms for stroke type diagnosis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6113 LNAI, Issue PART 1, 2010, Pages 651-656

  Naftulin, Igor S ^a   Rebrova, Olga Yu ^a  

^a RESEARCH CENTER OF NEUROLOGY   (Russian Federation)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ERRORS; FUZZY THRESHOLD; PRACTICAL USE; RULE SET; TRAINING SAMPLE;

AMPLIFICATION; ARTIFICIAL INTELLIGENCE; DATA MINING; DECISION TREES; SOFT COMPUTING;

LEARNING ALGORITHMS;

EID: 77955396643     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13208-7_81     Document Type: Conference Paper

References (13)

1. Kotova, E.Y.: Clinical and epidemiological characteristics, the leading risk factors, the features of the of stroke in Ulyanovsk, Russia (according to the Register of stroke). Abstract of PhD thesis, Moscow, p. 25 (2009) (in Russian)
2. Gusev, E.I., et al.: Epidemiology of stroke in Russia. J. Neurol. Psychiatry, Suppl. 103(8), 4-9 (2003) (in Russian)
3. Wardlaw, J.M., Keir, S.L., Dennis, M.S.: The impact of delays in computed tomography of the brain on the accuracy of diagnosis and subsequent management in patients with minor stroke. J. Neurol. Neurosurg. Psychiatry 74(1), 77-81 (2003)
4. Saur, D., Kucinski, T., Grzyska, U., et al.: Sensitivity and interrater agreement of CT and diffusion-weighted MR imaging in hyperacute stroke. Am J. Neuroradiol. 24(5), 878-885 (2003)
5. Kalafut, M.A., Schriger, D.L., Saver, J.L., Starkman, S.: Detection of early CT signs of 1 / 3 middle cerebral artery infarctions: interrater reliability and sensitivity of CT interpretation by physicians involved in acute stroke care. Stroke 31(7), 1667-1671 (2000)
6. Dippel, D.W., Du Ry van Beest Holle, M., van Kooten, F., Koudstaal, P.J.: The validity and reliability of signs of early infarction on CT in acute ischaemic stroke. Neuroradiology 42(9), 629-633 (2000)
7. Grotta, J.C., Chiu, D., Lu, M., Patel, S., et al.: Agreement and variability in the interpretation of early CT changes in stroke patients qualifying for intravenous rtPA therapy. Stroke 30(8), 1528-1533 (1999) (Pubitemid 29379597)
8. Yu, R.O.: Application of data mining to solve the problem of medical diagnostics. News of Artificial Intelligence (3), 76-80 (2004) (in Russian)
9. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Cole Advanced Books & Software. Wadsworth & Brooks, Monterey (1984)
10. Kass, G.V.: An exploratory technique for investigating large quantities of categorical data. Applied Statistics 29, 119-127 (1980)
11. Friedman, J.H.: Stochastic Gradient Boosting. Stanford University (1999)
12. Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data mining, Inference and Prediction. Springer, New York (2001)
13. Hyafil, L., Rivest, R.L.: Constructing Optimal Binary Decision Trees is NP - complete. Information Processing Letters 5(1), 15-17 (1976)