Applying data mining techniques to predict occupational accidents in the pulp and paper industry

被引：0

作者：

Mosquera R. ^{[1
]}

Parra L. ^{[2
]}

Ledesma A.J. ^{[3
]}

Bonilla H.F. ^{[4
]}

机构：

[1] Universidad de San Buenaventura, Cali-Facultad de Ingeniería, Nuevas tecnologías trabajo y gestión-Carrera 122 # 6-65, Cali

[2] Universidad Libre, Sede Bogotá, Facultad de Derecho, Centro de Investigaciones Socio Jurídicas, Grupo de Investigación Estudios Interdisciplinarios DESC y El Mundo Del Trabajo

[3] Universidad Icesi, Cali

[4] Pontificia Universidad Javeriana, Facultad de Ingeniería, Departamento de Ingeniería Civil e Industrial, Cali

来源：

Informacion Tecnologica | 2021年 / 32卷 / 01期

关键词：

Bayesian nets; Data mining; Decision-making trees; Naive; Occupational accidents; Paper; Pulp;

D O I：

10.4067/S0718-07642021000100133

中图分类号：

学科分类号：

摘要：

This research study proposes a classification system to identify and prevent occupational accidents in fiber storage warehouses at a pulp and paper facility. The present analysis is based on variables including pedestrian circulation, bobcat, trailer trucks, access, pedestrian circulation zones, and handrails. The proposed methodology defines and trains the system by using occupational accident event data collected at the facility. Three different predicting algorithms are used: J48 decision-making trees, Naive Bayes, and Bayesian nets. The results show that the J48 decision-making tree algorithm accurately identifies possible occupational accidents 90% of the time. It is concluded that identifying variables involved in occupational accidents allows generating a C4.5 (J48) decision-making tree that can be used as a support tool to prevent occupational accidents. © 2021 Centro de Informacion Tecnologica. All rights reserved.

引用

页码：133 / 142

页数：9

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