Psychosocial Factors Predict the Level of Substance Craving of People with Drug Addiction: A Machine Learning Approach

This study aimed to explore which factors had a greater impact on substance craving in people with substance use and the direction of the impact. A total of 895 male substance users completed questionnaires regarding substance craving, psychological security, positive psychological capital, interper...

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Autores principales:	Hua Gong, Chuyin Xie, Chengfu Yu, Nan Sun, Hong Lu, Ying Xie
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	substance craving life events aggression behavior impulsivity gradient boosting method Medicine R
Acceso en línea:	https://doaj.org/article/7f2485a5bd0b489ca26504aa4c6bb137
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spelling	oai:doaj.org-article:7f2485a5bd0b489ca26504aa4c6bb1372021-11-25T17:51:44ZPsychosocial Factors Predict the Level of Substance Craving of People with Drug Addiction: A Machine Learning Approach10.3390/ijerph1822121751660-46011661-7827https://doaj.org/article/7f2485a5bd0b489ca26504aa4c6bb1372021-11-01T00:00:00Zhttps://www.mdpi.com/1660-4601/18/22/12175https://doaj.org/toc/1661-7827https://doaj.org/toc/1660-4601This study aimed to explore which factors had a greater impact on substance craving in people with substance use and the direction of the impact. A total of 895 male substance users completed questionnaires regarding substance craving, psychological security, positive psychological capital, interpersonal trust, alexithymia, impulsivity, parental conflict, aggression behavior, life events, family intimacy, and deviant peers. Calculating the factor importance by gradient boosting method (GBM), found that the psychosocial factors that had a greater impact on substance craving were, in order, life events, aggression behavior, positive psychological capital, interpersonal trust, psychological security, impulsivity, alexithymia, family intimacy, parental conflict, and deviant peers. Correlation analysis showed that life events, positive psychological capital, interpersonal trust, psychological security, and family intimacy negatively predicted substance craving, while aggression behavior, impulsivity, alexithymia, parental conflict, and deviant peers positively predicted substance cravings. These findings have important implications for the prevention and intervention of substance craving behavior among substance users.Hua GongChuyin XieChengfu YuNan SunHong LuYing XieMDPI AGarticlesubstance cravinglife eventsaggression behaviorimpulsivitygradient boosting methodMedicineRENInternational Journal of Environmental Research and Public Health, Vol 18, Iss 12175, p 12175 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	substance craving life events aggression behavior impulsivity gradient boosting method Medicine R
spellingShingle	substance craving life events aggression behavior impulsivity gradient boosting method Medicine R Hua Gong Chuyin Xie Chengfu Yu Nan Sun Hong Lu Ying Xie Psychosocial Factors Predict the Level of Substance Craving of People with Drug Addiction: A Machine Learning Approach
description	This study aimed to explore which factors had a greater impact on substance craving in people with substance use and the direction of the impact. A total of 895 male substance users completed questionnaires regarding substance craving, psychological security, positive psychological capital, interpersonal trust, alexithymia, impulsivity, parental conflict, aggression behavior, life events, family intimacy, and deviant peers. Calculating the factor importance by gradient boosting method (GBM), found that the psychosocial factors that had a greater impact on substance craving were, in order, life events, aggression behavior, positive psychological capital, interpersonal trust, psychological security, impulsivity, alexithymia, family intimacy, parental conflict, and deviant peers. Correlation analysis showed that life events, positive psychological capital, interpersonal trust, psychological security, and family intimacy negatively predicted substance craving, while aggression behavior, impulsivity, alexithymia, parental conflict, and deviant peers positively predicted substance cravings. These findings have important implications for the prevention and intervention of substance craving behavior among substance users.
format	article
author	Hua Gong Chuyin Xie Chengfu Yu Nan Sun Hong Lu Ying Xie
author_facet	Hua Gong Chuyin Xie Chengfu Yu Nan Sun Hong Lu Ying Xie
author_sort	Hua Gong
title	Psychosocial Factors Predict the Level of Substance Craving of People with Drug Addiction: A Machine Learning Approach
title_short	Psychosocial Factors Predict the Level of Substance Craving of People with Drug Addiction: A Machine Learning Approach
title_full	Psychosocial Factors Predict the Level of Substance Craving of People with Drug Addiction: A Machine Learning Approach
title_fullStr	Psychosocial Factors Predict the Level of Substance Craving of People with Drug Addiction: A Machine Learning Approach
title_full_unstemmed	Psychosocial Factors Predict the Level of Substance Craving of People with Drug Addiction: A Machine Learning Approach
title_sort	psychosocial factors predict the level of substance craving of people with drug addiction: a machine learning approach
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7f2485a5bd0b489ca26504aa4c6bb137
work_keys_str_mv	AT huagong psychosocialfactorspredictthelevelofsubstancecravingofpeoplewithdrugaddictionamachinelearningapproach AT chuyinxie psychosocialfactorspredictthelevelofsubstancecravingofpeoplewithdrugaddictionamachinelearningapproach AT chengfuyu psychosocialfactorspredictthelevelofsubstancecravingofpeoplewithdrugaddictionamachinelearningapproach AT nansun psychosocialfactorspredictthelevelofsubstancecravingofpeoplewithdrugaddictionamachinelearningapproach AT honglu psychosocialfactorspredictthelevelofsubstancecravingofpeoplewithdrugaddictionamachinelearningapproach AT yingxie psychosocialfactorspredictthelevelofsubstancecravingofpeoplewithdrugaddictionamachinelearningapproach
_version_	1718411930068058112

Psychosocial Factors Predict the Level of Substance Craving of People with Drug Addiction: A Machine Learning Approach

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