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Young child injury analysis by the classification entropy method

1998; Elsevier BV; Volume: 30; Issue: 5 Linguagem: Inglês

10.1016/s0001-4575(97)00096-1

1879-2057

Marija Strnad, Franjo Jović, Ariana Vorko, Luka Kovačić, Drago Toth,

Injury Epidemiology and Prevention

The project 'The Register and Preventive Programs for Accidents and Injuries' enabled data collection on all the injured who sought medical aid in Koprivnica County (population 61,052), Croatia, since 1992. Children aged 1-4 years are 5.03% of the whole population of the district. Complex injury attributes were analysed. Binary attributes were classified as input: age, gender, place of injury; and output: severity of injury. A new application of information entropy was introduced and applied to the classification of injury-causes attributes. The information entropy was calculated for the classification of input attributes according to the minimum information content. The decision procedure is given as a sequential procedure separating important from unimportant causes of injury at each decision level. Thus a decision tree with increasing entropy, i.e. decreasing determinism, was obtained showing that age (0.5347 N), place (0.6062 N) and gender (0.6105 N) are measurable attributes in child injury ascertainment in a descending pattern. It was shown that this method is, at the same time, an optimal way of using an attribute decision process of injury causes classification.