Concept formation vs. logistic regression: predicting death in trauma patients.
This study compares two classification models used to predict survival of injured patients entering the emergency department. Concept formation is a machine learning technique that summarizes known examples cases in the form of a tree. After the tree is constructed, it can then be used to predict the classification of new cases. Logistic regression, on the other hand, is a statistical model that allows for a quantitative relationship for a dichotomous event with several independent variables. The outcome (dependent) variable must have only two choices, e.g. does or does not occur, alive or dead, etc. The result of this model is an equation which is then used to predict the probability of class membership of a new case. The two models were evaluated on a trauma registry database composed of information on all trauma patients admitted in 1992 to a Level I trauma center. A total of 2155 records. representing all trauma patients admitted for more than 24 h or who died in the Emergency Department, were grouped into two databases as follows: (1) discharge status of 'died' (containing 151 records), and (2) any discharge status other than 'died' (containing 2004 records). Both databases contained the same variables.
Hadzikadic
M
M
Carolinas Medical Center, Department of Orthopaedic Informatics, Charlotte, NC 28203, USA. mirsad@uncc.edu
Hakenewerth
A
A
Bohren
B
B
Norton
J
J
Mehta
B
B
Andrews
C
C
D001185:Artificial Intelligence; D016000:Cluster Analysis; D003210:Concept Formation; D004636:Emergency Service, Hospital; D006801:Humans; D016015:Logistic Models; D011237:Predictive Value of Tests; D012044:Regression Analysis; D013534:Survival; D016896:Treatment Outcome; D014947:Wounds and Injuries / Q000401:mortality*