Extracting the Factors Affecting the Survival Rate of Trauma Patients Using Data Mining Techniques on a National Trauma Registry

Thousands of people die due to trauma all over the world every day, which leaves adverse effects on families and the society. The main objective of this study was to identify the factors affecting the mortality of trauma patients using data mining techniques.

The present study includes six parts: data gathering, data preparation, target attributes specification, data balancing, evaluation criteria, and applied techniques. The techniques used in this research are all from the decision tree family. The output of these techniques are patterns extracted from the trauma patients dataset (National Trauma Registry of Iran). The dataset includes information on 25,986 trauma patients from all over the country. The techniques that were used include random forest, CHAID, and ID3.

Random forest performs better than the other two techniques in terms of accuracy. The ID3 technique performs better than the other two techniques in terms of the dead class. The random forest technique has performed better than other techniques in the living class. The rules with the most support, state that if the Injury Severity Score (ISS) is minor and vital signs are normal, 98% of people will survive. The second rule, in terms of support, states that if ISS is minor and vital signs are abnormal, 93% will survive. Also, by increasing the threshold of the patient's arrival time from 10 to 15 minutes, no noticeable difference was observed in the death rate of patients.

Transfer time of less than ten minutes in patietns whose ISS is minor, can increase the chance of survival. Impaired vital signs can decrease the chance of survival in traffic accidents. Also, if the ISS is minor in non-penetrating trauma, regardless of vital signs and if the victim is transported in less than ten minutes, the patient will survive with 99% certainty.