Data mining trauma injury data using C5.0 and logistic regression to determine factors associated with death.
Chesney, Thomas; Penny, Kay I; Chesney, David; Oakley, Peter; Viglas, S; Templeton, John; Maffulli, Nicola
Kay I Penny
Trauma injury data collected over 10 years at a UK hospital are analysed. The data include injury details such as patient age and gender, the mechanism of injury, various measures of injury severity, management interventions, and treatment outcome. Logistic regression modelling was used to determine which factors were independently associated with death during hospital stay. The data mining algorithm C5.0 was also used to determine those factors in the data that can be used to predict whether a patient will live or die. Logistic modelling and C5.0 show that different subsets of injury severity scores, and patient age, are associated with survival. In addition, C5.0 also shows that gender, and whether the patient was referred from another hospital, is important. The two techniques give different insights into those factors associated with death after trauma
Chesney, T., Penny, K. I., Chesney, D., Oakley, P., Viglas, S., Templeton, J., & Maffulli, N. (2009). Data mining trauma injury data using C5.0 and logistic regression to determine factors associated with death. International Journal of Healthcare Technology and Management, 10, 16-26. doi:10.1504/IJHTM.2009.023725
|Journal Article Type||Article|
|Deposit Date||Mar 21, 2012|
|Peer Reviewed||Peer Reviewed|
|Keywords||: C5.0 algorithm; data mining; logistic regression; trauma injury data; UK; United Kingdom; healthcare; patient age; patient gender; injury mechanism; injury severity; management interventions; treatment outcome; patient survival; death factors.|
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