Title: Applying decision trees on road traffic accident data for predicting the survival chance of patients

Authors: Parham Porouhan

Addresses: Graduate School of Information Technology, Siam University, Bangkok, Thailand

Abstract: The main objective of this study is to generate decision tree (DT) models/graphs (i.e., a type of supervised machine learning (ML) research method) through RapidMiner Studio (i.e., a popular visual platform for predictive analytics). The dataset used in the study contains attributes regarding the car accidents such as 'gender', 'casualty class', 'age group' and 'type of vehicle'. These are important features to decide whether the 'survival chance' of traffic accident patients would be 'high' or 'low'. Therefore, our goal is to apply 'DTs' for predicting the 'survival attribute' with the purpose of identifying high risk groups within the dataset. The resulting 'DTs' show that whenever the attribute 'gender' has the value 'male', and the attribute 'casualty class' has the value 'passenger', and the attribute 'gender' has the value 'male', and the attribute 'age group' has the value 'teenager'; then the 'survival chance' of the traffic/accident patient would be extremely 'low'.

Keywords: data mining; supervised machine learning; RapidMiner studio; decision tree; DT; Bayesian networks; predictive analytics; accident risk factors; survival rate.

DOI: 10.1504/IJKEDM.2023.135710

International Journal of Knowledge Engineering and Data Mining, 2023 Vol.8 No.1, pp.1 - 26

Received: 26 Jul 2022
Accepted: 16 Aug 2022
Published online: 03 Jan 2024 *

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