Predicting survival causes after out of hospital cardiac arrest using data mining method.
BACKGROUND
The prognosis of life for patients with heart failure remains poor. By using data mining methods, the purpose of this study was to evaluate the most important criteria for predicting patient survival and to profile patients to estimate their survival chances together with the most appropriate technique for health care.


METHODS
Five hundred and thirty three patients who had suffered from cardiac arrest were included in the analysis. We performed classical statistical analysis and data mining analysis using mainly Bayesian networks.


RESULTS
The mean age of the 533 patients was 63 (+/- 17) and the sample was composed of 390 (73 %) men and 143 (27 %) women. Cardiac arrest was observed at home for 411 (77 %) patients, in a public place for 62 (12 %) patients and on a public highway for 60 (11 %) patients. The belief network of the variables showed that the probability of remaining alive after heart failure is directly associated to five variables: age, sex, the initial cardiac rhythm, the origin of the heart failure and specialized resuscitation techniques employed.


CONCLUSIONS
Data mining methods could help clinicians to predict the survival of patients and then adapt their practices accordingly. This work could be carried out for each medical procedure or medical problem and it would become possible to build a decision tree rapidly with the data of a service or a physician. The comparison between classic analysis and data mining analysis showed us the contribution of the data mining method for sorting variables and quickly conclude on the importance or the impact of the data and variables on the criterion of the study. The main limit of the method is knowledge acquisition and the necessity to gather sufficient data to produce a relevant model.
Le Duff
Franck
F
Medical Informatics Laboratory, Medical School, Av du Pr Léon Bernard, Rennes Cedex France. Franck.leduff@univ-rennes1.fr
Muntean
Cristian
C
Cuggia
Marc
M
Mabo
Philippe
P
D001499:Bayes Theorem; D016887:Cardiopulmonary Resuscitation; D003627:Data Interpretation, Statistical; D003663:Decision Trees; D004632:Emergency Medical Services; D005260:Female; D006323:Heart Arrest / Q000401:mortality* / Q000628:therapy; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011336:Probability; D011379:Prognosis; D012189:Retrospective Studies; D012306:Risk; D015996:Survival Rate