Implementation of a fuzzy prototype-based machine learning method to predict myocardial infarction from coronary angiography

Studies in Health Technology and Informatics

Volumn 52, Issue , 1998, Pages 498-502

  Colombet, Isabelle ^a   Jaulent, Marie Christine ^a   Diebold, Benoît ^a   Degoulet, Patrice ^a  

^a HÔPITAL BROUSSAIS   (France)

Author keywords

Coronary Artery Disease; Fuzzy logic; Symbolic Learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; CARDIOLOGY; DISEASES; FUZZY LOGIC; NEAREST NEIGHBOR SEARCH; RISK PERCEPTION; SUPERVISED LEARNING;

CORONARY ARTERY DISEASE; MACHINE LEARNING METHODS; MEASURE OF SIMILARITIES; MORPHOLOGICAL CHARACTERISTIC; MORPHOLOGICAL DESCRIPTION; MYOCARDIAL INFARCTION; SUPERVISED MACHINE LEARNING; SYMBOLIC LEARNING;

LEARNING ALGORITHMS;

ANGIOCARDIOGRAPHY; ARTICLE; ARTIFICIAL INTELLIGENCE; CORONARY ARTERY DISEASE; DATA BASE; EVALUATION; FUZZY LOGIC; HEART INFARCTION; HUMAN; RADIOGRAPHY;

ARTIFICIAL INTELLIGENCE; CORONARY ANGIOGRAPHY; CORONARY DISEASE; DATABASES; EVALUATION STUDIES; FUZZY LOGIC; HUMANS; MYOCARDIAL INFARCTION;

EID: 84887630909     PISSN: 09269630     EISSN: 18798365     Source Type: Book Series    
DOI: 10.3233/978-1-60750-896-0-498     Document Type: Conference Paper

References (14)

1. Nwasokwa ON, Koss JH, Friedman GH, Grunwald Boden heimer MM.Bypass surgery for chronic stable angina: predictors of survival benefit and strategy for patient selection.Ann Intern Med.1991;114:1035-49.
2. Little WC, Constantinescu M, Applegate RJ, Kutcher M, Santamore WP.Can coronary angiography predict The site of A subsequent myocardial infarction in patients with mild to moderate coronary artery disease? Circulation.1988;67:283- 90.
3. Ledru F, Theroux P, Lesperance J, Laurier J, Ducimetiere P, Guermonprez JL, Diebold B.Geometric features of coronary artery lesions favoring acute thrombotic occlusion and myocardial infarction.A quantative coronary angiographic study, (in press)
4. Cornuejols A, Moulet M, Vincent R.Dossier apprentis-sage symbolique automatique.Bulletin de I'AFIA.1995;22:14-44.
5. Cooper GF, Aliferis CF, Ambrosino R, Aronis J, Buchanan BG, Caruana R, Fine MJ, Glymour C, Gordon G, Hanusa BH, Janosky JE, Meek C, Mitchell T, Richardson T, Spirtes P.An evaluation of machine-learning methods for predicting pneumonia mortality.Artif Intell Med.1997;9:107-38
6. Von-Wun Soo, Wang Jan-Sing., Shih-Pu Wang, Learning and discovery from A clinical database: An incremental concept formation approach.Art Int Med.1994;6:249-61
7. Fayyad UM.Data mining and knowledge discovery: making sense out of data.IEEE Expert Intelligent Syst and their applications.Oct 1996.pp 20-5
8. Hadzicadic M, Hakenewerth A, Bohren B, Norton J, Mehta B, Andrews C.Concept formation vs logistic regression: predicting death in trauma patients.Art Intell Med 1996;8:493-504.
9. Kuncheva L.Fuzzy two-level classifier for high-G analysis.Medical diagnosis of acceleration effects.IEEE Engineering in Medicine and Biology.1994; Nov/Dec:717-22.
10. Rifqui Maria.Mesure de comparaison, typicalite et classification d'objets fious: theorie et pratique.PhD Thesis.Paris VI University.1996.
11. Zadeh LA.Fuzzy sets.Information and Control, 1965; vol 8, pp338-53.
12. Zadeh LA.Fuzzy sets as A basis for A theory of possibility.Fuzzy sets and Systems.Vol.1,1978; pp3~28
13. Dubois D,.Prade H.Fuzzy Set and Possibility Theory: recent developments.Eds R.Yager, Pergman Press, Oxford.1982;pp3-13.
14. Lesperance J, Theroux P, Hudon G, Waters D.A new look at coronary angiograms: plaque morphology as A help to diagnosis and to evaluate outcomes.Int J Card Imag.1994;10:75-94