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

Gong, Hua; Xie, Chuyin; Yu, Chengfu; Sun, Nan; Hong, Liu; Xie, Ying

doi:10.3390/ijerph182212175

Cited by 8 publications

(6 citation statements)

References 67 publications

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“…Numerous studies have shown a high level of ,anxiety, and aggression in people with relapse. , A low level of interpersonal trust and insecurity, A low level of interpersonal trust and insecurity, predicts a high level of anxious and aggressive behavior [23], [24]. Chauhan, Nautiyal, Garg, and Singh research to find out predictors of relapse in cases of opioid/alcohol dependence.…”

Section: Discussionmentioning

confidence: 99%

“…A study conducted in China suggests that low self-efficacy predicts relapse in drug addiction. The relationship between the individuals who face relapse and their blood relations should be raised, and the rehabilitation center should take various measures to improve the psychological capital level and the level of self-awareness of the people who frequently use drugs, and it will be helpful to reduce their relapse rate [16].…”

Section: International Scenariosmentioning

confidence: 99%

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Psychosocial Factors as the Determinants of Relapse in Individuals with Substance Use Disorder

Farooq

Riaz

2022

IJIST

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Relapse to substance abuse after withdrawal is one of the most perplexing and frustrating aspect of addiction. The word relapse defines as collapse/hindrance to the client’s prior substance dependence behavior after treatment due to many psychological, social, and other associated factors. This study is designed to find out the psychological and social factors which determine relapse behavior in individuals with substance use disorder. A cross-sectional study was conducted among 200 relapse patients from November 2020 to January 2021 in different rehabilitations in Wazirabad, Gujranwala, Lahore, Gujrat, and Sialkot cities of Pakistan. Data were collected by using a psychosocial functioning scale. Data analyzed using SPSS 21. A total of 200 individuals were taken ,of which 170 were males and 30 females. The results indicates that the most important predictor was risk-taking 0.148 (100% of normalized importance) followed by social conformity 0.130 (88% of normalized importance), decision making confidence 0.128 (86.2% of normalized importance), childhood problem 0.125 (84.4% of normalized importance), hostility 0.125 (84.0% of normalized importance), depression 0.119 (80.5% of normalized importance), self-esteem 0.114 (76.6% of normalized importance) and anxiety 0.111 (74.7% of normalized importance).Social factors were connected with a high relapse rate. These results may help clinicians to improve treatment and policy guidelines for the prevention of relapse to drug addiction.

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Section: Discussionmentioning

confidence: 99%

Section: International Scenariosmentioning

confidence: 99%

Psychosocial Factors as the Determinants of Relapse in Individuals with Substance Use Disorder

Farooq

Riaz

2022

IJIST

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“…The literature work discussed various applications of classi cation algorithms and machine learning techniques in various elds such as medical diagnosis[6]-[8], substance abuse [9], and diabetes[6], [8], [10], [11] prediction. In the study [12], authors used anthropometric data and blood analysis parameters to classify patients with breast cancer using arti cial neural networks and Naive Bayes classi ers, with high potential for accurate diagnosis.…”

Section: Ict In Medicinementioning

confidence: 99%

“…In the study [12], authors used anthropometric data and blood analysis parameters to classify patients with breast cancer using arti cial neural networks and Naive Bayes classi ers, with high potential for accurate diagnosis. The psychosocial factors [9] such as life events, aggression behavior, and interpersonal trust have a greater impact on substance craving in male substance users. The authors in [10] introduced a new labeled diabetes dataset from Bangladesh and proposed an automated classi cation pipeline using a weighted ensemble of machine learning classi ers, which produced the best results for early diabetes prediction.…”

Section: Ict In Medicinementioning

confidence: 99%

Development and Evaluation of a Predictive Diagnostic System for Dengue Fever using Machine Learning Techniques

Khan

Farooqui

Raza

et al. 2023

Preprint

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As the world continues to evolve scientifically and climatically, the medical and healthcare sectors face new challenges in the form of newly identified diseases, insects, chemicals, and environmental effects. These new challenges are often more complex and dangerous than existing conditions, and require prompt diagnosis and appropriate treatment to prevent loss of life. This paper presents a study on the development of a presumptive diagnosis system (PDS) for early dengue fever detection. The proposed system aims to assist medical professionals in determining the disease at an early stage and help unprofessional or new staff to learn and experiment better. The PDS utilizes machine learning algorithms such as Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB), along with their combination, to classify dengue fever and its types. The dataset used in this study consisted of 400 records of 15 attributes, and the data was preprocessed to reduce noise, incompleteness and inconsistencies. The classification results showed that the PDS achieved an average accuracy of 95.6%, with the highest individual accuracy achieved by RF, DT and NB being 98%, 97% and 87% respectively. Our results demonstrate that PDS can facilitate the diagnostic process in resource-limited settings by providing an alternative to hardware-intensive solutions. Furthermore, the system can aid healthcare professionals in making early and accurate diagnoses, and can also serve as a learning tool for new staff.

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“…Psychosocial factors are multidimensional constructing, encompassing two different domains as psychological domain (depression, anxiety, distress, self-esteem, and satisfaction) and the social domain (socioeconomic status, employment, religion, physical attributes, family, relationship with others, locality). These factors contribute to the development of mental and physical disorders [12], [13]. Psychosocial factors influence an individual psychologically or socially.…”

Section: Introductionmentioning

confidence: 99%

Psycho-Social and Morbidity of Substance Use Disorder in Women

Iftikhar

Riaz

2022

IJIST

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Substance abuse disorder is a major and worldwide concern that cursed countries and mankind. Psychosocial factors influences differ across the person and may contribute to the development of physical and mental disorders. The research aimed to investigate the impact of psychological factors (Self-esteem, Depression, Anxiety, and Decision-Making Confidence) and social factors (Childhood Problems, Hostility, Risk-taking, and Social Conformity) that predictors of substance use disorder in women. en cross-sectional survey design was used in this study. Drug Abuse Screaming Test (DAST) and psychosocial functioning scale were used to collect data on women (N=200). The purposive sampling technique was employed for sample selection; moreover, the snowball technique was also used as the drug-addicted women recommended the other women. Results of the study ravels that psychosocial factors were a significant predictor of substance use disorder in women. The finding of the multiple regression analysis reveals that psychosocial factors were significant predictors of substance use disorder in women [R2 =.46, F (1,142)14.26, p<.01]. In conclusion, this study highlights some psychological (Self-esteem, Anxiety, Decision-making confidence) and social factors (Childhood problems, Risk-taking, and social conformity) that are valuable predictors of substance use disorder in women. These findings may help clinicians to develop treatment and policy guidelines for the prevention of drug addiction in women.

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

Cited by 8 publications

References 67 publications

Psychosocial Factors as the Determinants of Relapse in Individuals with Substance Use Disorder

Psychosocial Factors as the Determinants of Relapse in Individuals with Substance Use Disorder

Development and Evaluation of a Predictive Diagnostic System for Dengue Fever using Machine Learning Techniques

Psycho-Social and Morbidity of Substance Use Disorder in Women

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