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Table 2 Trauma dataset features

From: Extraction frequent patterns in trauma dataset based on automatic generation of minimum support and feature weighting

Features

Categories

Age

Child, teenager, young, middle-aged and elderly

Type of conveyance carrying to emergency

ambulance, taxi, personal vehicle

Place birth

city, village

Total expenditures

 Type of insurance

treatment services, social security, military, bank, free, others

 Number of days admitted

One day, two days, three days and more

 Sex

male, female

 ICD-injuries

Pedestrian injured in transport accident, Pedal cycle rider injured in transport accident, Motorcycle rider injured in transport accident, Car occupant injured in transport accident, Water transport accidents, Slipping, tripping, stumbling and falls, Exposure to electric current, radiation and extreme ambient air temperature and pressure

 Occupation

child, staff, worker, farmer, unemployed, students, businessmen, housewives, others job

 ICD-external causes

Injuries to the head, Injuries to the abdomen, lower back, lumbar spine, pelvis and external genitals, Injuries to the shoulder and upper arm, Injuries to the elbow and forearm, Injuries to the wrist, hand and fingers, Injuries to the hip and thigh, Injuries to the knee and lower leg, Injuries to the ankle and foot

 Education

child, illiterate, school, high school, after diploma

 State of discharge

non-improvement, improvement