Novel Hybrid Trauma Injury Classification based on Trauma Revise Injury Severity Score (TRISS) and Visum et Repertum (VeR) Features

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Mohd Hadyan Wardhana, Abd Samad Hasan Basari, Nuzulha Khilwani Ibrahim, Wan Zulaikha Wan Yaacob
DOI : 10.14445/22315381/IJETT-V70I7P248

How to Cite?

Mohd Hadyan Wardhana, Abd Samad Hasan Basari, Nuzulha Khilwani Ibrahim, Wan Zulaikha Wan Yaacob, "Novel Hybrid Trauma Injury Classification based on Trauma Revise Injury Severity Score (TRISS) and Visum et Repertum (VeR) Features" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 462-470, 2022. Crossref,

Trauma injury classification is crucial for refining diagnostic and increasing the accuracy of Forensic and Medicolegal services. Current approaches are considered challenging in determining the critical features. Insufficient critical features analysis causes inconsistent judgment in analyzing the degree of trauma injury among the medical consultants. The issue becomes more complicated because the dataset consists of incomplete data and outliers class problems that can affect the sampling bias. The objective of this research is to identify the features and terms of trauma injury by combining the Trauma Revise Injury Severity Score (TRISS) with Visum et Repertum (VeR) Data, develop and evaluate the Hybrid Neural Network Model (HNNM) for classifying the degree of trauma injury for an Indonesian use case. The sample data consists of the TRISS features, including physiological and anatomical information. While the VeR data was used to improve the critical features selection. The HNNM is expected to classify the persecution victim as having a minor, moderate, or major injury, as defined by the Indonesian Penal Code. HNNM is proposed based on the case studies at three hospitals in the Indonesian city of Pekanbaru. It comprises three main phases: pre-processing, development, and performance analysis. Pre-processing phases solve the drawbacks of incomplete data by performing data cleansing and normalization. Then, the features' determination is chosen by utilizing the Neural Network (NN) as a classification algorithm and the Genetic Algorithm (GA) as an optimization technique. The selected features are applied during the dataset training stages to increase the HNNM's accuracy and minimize error. GA's goal is to increase the accuracy rate and reduce error in the learning stages of NN. The development phase is accomplished with testing stages by combining the TRISS feature and the VeR dataset. The performance analysis shows the HNNM produced a 98.85% accuracy level and the Root Mean Square Error (RMSE) value at 0.077. In the validation stage, the features of the HNNM are implementable and highly acceptable by the practitioner. For future works, the HNNM needs to increase the accuracy by improving the input features, including lifestyle, habit, and job.

Trauma Revise Injury Severity Score (TRISS), Visum et Repertum (VeR), Medicolegal Prognostic, Root Mean Square Error (RMSE), Analytical Hierarchical Process (AHP).

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