Case-Based Reasoning Approach to Map Similar Cases for Accident Injury Claims

  • Authors

    • Shuhaizan Sulaiman
    • Nurzeatul Hamimah Abdul Hamid
    • Nur Huda Jaafar
    • Shuzlina Abdul-Rahman
    2018-12-09
    https://doi.org/10.14419/ijet.v7i4.31.23390
  • Accident claim case, Case-based reasoning, Knowledge-based systems, Legal advisor system.

  • Abstract

    The procedures of getting compensation from a legal claim are time-consuming. Legal practitioners spend the time to retrieve the similar or relevant past cases to support their claim for a case. The process to map the past similar cases to get the best claim is even more challenging. Thus, this study aims to help the legal practitioners in their work to speed-up the process of claiming for compensation in an accident case. Consequently, it helps those who need and worthy of the compensated money. This study developed a Legal Advisor of Accident Cases system using a CBR approach which outlines three primary objectives; (i) to identify similar past cases features to the case in-progress; (ii) to design an intelligent component to assist legal practitioners in finding similar cases to support an injury claim case; (iii) to develop a prototype of the legal advisor system that can be used by legal practitioners to speed-up the case settlement. Data for this project has been collected from a law firm cases and a series of interview sessions with legal practitioners. It reused the past cases and aligned with the legal procedures. The system performance with an efficiency rate of 75% has been achieved. It has a potential to be extended to cover a wide area of legal claims.

     

     

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  • How to Cite

    Sulaiman, S., Hamimah Abdul Hamid, N., Huda Jaafar, N., & Abdul-Rahman, S. (2018). Case-Based Reasoning Approach to Map Similar Cases for Accident Injury Claims. International Journal of Engineering & Technology, 7(4.31), 326-330. https://doi.org/10.14419/ijet.v7i4.31.23390

    Received date: 2018-12-07

    Accepted date: 2018-12-07

    Published date: 2018-12-09