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.
|Title of host publication||2017 36th International Conference of the Chilean Computer Science Society, SCCC 2017|
|Publisher||IEEE Computer Society|
|Number of pages||6|
|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
|Name||Proceedings - International Conference of the Chilean Computer Science Society, SCCC|
|Conference||36th International Conference of the Chilean Computer Science Society, SCCC 2017|
|Period||16/10/17 → 20/10/17|
Bibliographical notePublisher Copyright:
© 2017 IEEE.
- Suicide tendency
- machine learning
- suicide attempt
- suicide risk