02025nas a2200313   4500000000100000000000100001008004100002260001200043653002500055653001600080653001800096653003700114653002500151653001800176653003700194653002900231100001300260700001900273700001300292700001600305700001300321700001600334245009400350856008100444300000900525490001300534520115000547022001401697       9998                            d  c02/202410aCase Based Reasoning10aData Mining10aDecision Tree10aMedical Decision Making Approach10aPredictive Modelling10aRoad Accident10aSelection of Relevant Attributes10aTraumatic Brain Injuries1 aF. Saadi1 aBaghdad Atmani1 aF. Henni1 aH. Benfriha1 aZ. Addou1 aR. Guerbouz00aAn Effective Prediction Approach for the Management of Children Victims of Road Accidents  uhttps://www.ijimai.org/journal/sites/default/files/2024-02/ip2024_02_001.pdf  a1-110 vIn press3 aRoad traffic generates a considerable number of accidents each year. The management of injuries caused by these accidents is becoming a real public health problem. Faced with this latter, we propose a new clinical decision making approach based on case-based reasoning (CBR) and data mining (DM) techniques to speed up and improve the care of an injured child. The main idea is to preprocess the dataset before using K Nearest Neighbor (KNN) Classification Model. In this paper, an efficient predictive model is developed to predict the admission procedure of a child victim of a traffic accident in pediatric intensive care units. The evaluation of the proposed model is conducted on a real dataset elaborated by the authors and validated by statistical analysis. This novel model executes a selection of relevant attributes using data mining technique and integrates a CBR system to retrieve similar cases from an archive of cases of patients successfully treated with the proposed treatment plan. The results revealed that the proposed approach outperformed other models and the results of previous studies by achieving an accuracy of 91.66%.  a1989-1660