Unveiling the Causes of Fatal Road Accidents in Iraq: An Association Rule Mining Approach Using the Apriori Algorithm

With the increase in fatal accidents in Iraq, they have become a source of concern for both authorities and the public. Therefore, it has become necessary to conduct an analysis of these road accidents. This study aims to provide recommendations to responsible authorities after assessing the frequency of fatal traffic accidents and identifying the most common causes. This will provide actionable insights for decision-makers to formulate laws that allow for the reduction of these accidents and the reduction of human and economic losses. This paper applied data mining algorithms to three years of traffic fatal accident data in Iraq, excluding the Kurdistan Region. The results showed that people without driver's licenses and with primary school certificates were more likely to fail to wear seatbelts, making them a dangerous group. Married individuals aged 36-41 were also associated with fatal accidents. Based on the results, some recommendations were made to reduce these accidents.

Authors

Publication Details

Journal
Journal of Cyber Security and Risk Auditing
Published
2025-04-02
DOI
https://doi.org/10.63180/jcsra.thestap.2025.2.1
Citations
7
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
20.56
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article

Unveiling the Causes of Fatal Road Accidents in Iraq: An Association Rule Mining Approach Using the Apriori Algorithm

Salam Al-Augby, Malath Riyadh Alboalebrah
7 citations
Journal of Cyber Security and Risk Auditing
Artificial Intelligence in Healthcare
20.56
article

Unveiling the Causes of Fatal Road Accidents in Iraq: An Association Rule Mining Approach Using the Apriori Algorithm

Salam Al-Augby, Malath Riyadh Alboalebrah
article en
7 citations

Abstract

With the increase in fatal accidents in Iraq, they have become a source of concern for both authorities and the public. Therefore, it has become necessary to conduct an analysis of these road accidents. This study aims to provide recommendations to responsible authorities after assessing the frequency of fatal traffic accidents and identifying the most common causes. This will provide actionable insights for decision-makers to formulate laws that allow for the reduction of these accidents and the reduction of human and economic losses. This paper applied data mining algorithms to three years of traffic fatal accident data in Iraq, excluding the Kurdistan Region. The results showed that people without driver's licenses and with primary school certificates were more likely to fail to wear seatbelts, making them a dangerous group. Married individuals aged 36-41 were also associated with fatal accidents. Based on the results, some recommendations were made to reduce these accidents.

Journal of Cyber Security and Risk AuditingVol. 2025(2)
Openalex Percentile: Top 1%
Artificial Intelligence in Healthcare
20.56
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Unveiling the Causes of Fatal Road Accidents in Iraq: An Association Rule Mining Approach Using the Apriori Algorithm — Salam Al-Augby, Malath Riyadh Alboalebrah · Journal of Cyber Security and Risk Auditing (2025) | TGRS Research Map | TGRS