Abstract
Suicide is one of the most distinguished causes of death on the news worldwide. There are several factors and variables that can lead a person to commit this act, for example, stress, self-esteem, depression, among others. The causes and profiles of suicide cases are not revealed in detail by the competent institutions. We propose a simulation with a systematically generated dataset; such data reflect the adolescent population with suicidal tendency in Peru. We will evaluate three algorithms of supervised machine learning as a result of the algorithm C4.5 which is based on the trees to classify in a better way the suicidal tendency of adolescents. We finally propose a desktop tool that determines the suicidal tendency level of the adolescent.
Original language | English |
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Title of host publication | 2017 36th International Conference of the Chilean Computer Science Society, SCCC 2017 |
Publisher | IEEE Computer Society |
Pages | 1-6 |
Number of pages | 6 |
ISBN (Electronic) | 9781538634837 |
DOIs | |
State | Published - 5 Jul 2018 |
Event | 36th International Conference of the Chilean Computer Science Society, SCCC 2017 - Arica, Chile Duration: 16 Oct 2017 → 20 Oct 2017 |
Publication series
Name | Proceedings - International Conference of the Chilean Computer Science Society, SCCC |
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Volume | 2017-October |
ISSN (Print) | 1522-4902 |
Conference
Conference | 36th International Conference of the Chilean Computer Science Society, SCCC 2017 |
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Country/Territory | Chile |
City | Arica |
Period | 16/10/17 → 20/10/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Keywords
- Suicide tendency
- classification
- machine learning
- prevention
- suicide
- suicide attempt
- suicide risk