Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System
Suicidal behavior among young people has become an increasingly relevant topic after the COVID-19 pandemic and constitutes a public health problem. This study aimed to examine the variables associated with suicide risk and determine their predictive capacity. The specific objectives were: (1) to ana...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/11/16/2337 |
| _version_ | 1797584540770762752 |
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| author | Francisco Manuel Morales-Rodríguez Juan Pedro Martínez-Ramón José Miguel Giménez-Lozano Ana María Morales Rodríguez |
| author_facet | Francisco Manuel Morales-Rodríguez Juan Pedro Martínez-Ramón José Miguel Giménez-Lozano Ana María Morales Rodríguez |
| author_sort | Francisco Manuel Morales-Rodríguez |
| collection | DOAJ |
| description | Suicidal behavior among young people has become an increasingly relevant topic after the COVID-19 pandemic and constitutes a public health problem. This study aimed to examine the variables associated with suicide risk and determine their predictive capacity. The specific objectives were: (1) to analyze the relationship between suicide risk and model variables and (2) to design an artificial neural network (ANN) with predictive capacity for suicide risk. The sample comprised 337 youths aged 18–33 years. An ex post facto design was used. The results showed that emotional attention, followed by problem solving and perfectionism, were variables that contributed the most to the ANN’s predictive capacity. The ANN achieved a hit rate of 85.7%, which is much higher than chance, and with only 14.3% of incorrect cases. This study extracted relevant information on suicide risk and the related risk and protective factors via artificial intelligence. These data will be useful for diagnosis as well as for psycho-educational guidance and prevention. This study was one of the first to apply this innovative methodology based on an ANN design to study these variables. |
| first_indexed | 2024-03-10T23:54:06Z |
| format | Article |
| id | doaj.art-2eb7e6646508449b95e3f8bd186961e4 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-10T23:54:06Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-2eb7e6646508449b95e3f8bd186961e42023-11-19T01:19:34ZengMDPI AGHealthcare2227-90322023-08-011116233710.3390/healthcare11162337Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network SystemFrancisco Manuel Morales-Rodríguez0Juan Pedro Martínez-Ramón1José Miguel Giménez-Lozano2Ana María Morales Rodríguez3Department of Educational and Developmental Psychology, Faculty of Psychology, University of Granada, 18011 Granada, SpainDepartment of Evolutionary and Educational Psychology, Faculty of Psychology and Speech Therapy, Campus Regional Excellence Mare Nostrum, University of Murcia, 30100 Murcia, SpainDepartment of Educational and Developmental Psychology, Faculty of Psychology, University of Granada, 18011 Granada, SpainDepartment of Journalism, Faculty of Communication Sciences, University of Málaga, 29010 Malaga, SpainSuicidal behavior among young people has become an increasingly relevant topic after the COVID-19 pandemic and constitutes a public health problem. This study aimed to examine the variables associated with suicide risk and determine their predictive capacity. The specific objectives were: (1) to analyze the relationship between suicide risk and model variables and (2) to design an artificial neural network (ANN) with predictive capacity for suicide risk. The sample comprised 337 youths aged 18–33 years. An ex post facto design was used. The results showed that emotional attention, followed by problem solving and perfectionism, were variables that contributed the most to the ANN’s predictive capacity. The ANN achieved a hit rate of 85.7%, which is much higher than chance, and with only 14.3% of incorrect cases. This study extracted relevant information on suicide risk and the related risk and protective factors via artificial intelligence. These data will be useful for diagnosis as well as for psycho-educational guidance and prevention. This study was one of the first to apply this innovative methodology based on an ANN design to study these variables.https://www.mdpi.com/2227-9032/11/16/2337suicidesuicide riskyouthartificial neural networkartificial intelligenceprotective and risk factors |
| spellingShingle | Francisco Manuel Morales-Rodríguez Juan Pedro Martínez-Ramón José Miguel Giménez-Lozano Ana María Morales Rodríguez Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System Healthcare suicide suicide risk youth artificial neural network artificial intelligence protective and risk factors |
| title | Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System |
| title_full | Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System |
| title_fullStr | Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System |
| title_full_unstemmed | Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System |
| title_short | Suicide Risk Analysis and Psycho-Emotional Risk Factors Using an Artificial Neural Network System |
| title_sort | suicide risk analysis and psycho emotional risk factors using an artificial neural network system |
| topic | suicide suicide risk youth artificial neural network artificial intelligence protective and risk factors |
| url | https://www.mdpi.com/2227-9032/11/16/2337 |
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