Prediction of physical violence in schizophrenia with machine learning algorithms

Wang, Kevin Z; Bani‐Fatemi, Ali; Adanty, Christopher; Harripaul, Ricardo; Griffiths, John; Kolla, Nathan J.; Gerretsen, Philip; Graff, Ariel; Luca, Vincenzo De

doi:10.1016/j.psychres.2020.112960

Cited by 26 publications

(57 citation statements)

References 43 publications

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“…Through comparing with each other, the nnet had better performance, and its AUC of 0.6673 (0.5599-0.7748) was significantly better than chance. In terms of the ability to recognize male schizophrenia patients with violence, our model performance showed similar precision as was obtained in the previous studies ( 14 , 22 ). Moreover, the nnet algorithm can calculate the probability of an individual committing violence.…”

Section: Discussionsupporting

confidence: 84%

“…ML algorithms have been proved to be an effective method for predicting violent behavior among schizophrenia patients. For instance, Wang et al utilized seven classification algorithms to predict violence status in schizophrenia individuals and found random forests showed better performance, its accuracy and AUC achieving 62% and 0.63, respectively ( 14 ). Another study determined gradient boosting as the best algorithm among seven algorithms, with its accuracy and AUC being 0.678 and 0.764 in predicting violent offending of forensic offender patients with schizophrenia, respectively ( 22 ).…”

Section: Discussionmentioning

confidence: 99%

“…So far, there have been two studies that employed ML algorithms to predict violent behavior in individuals with schizophrenia ( 14 ). In both studies, gender was included as a risk factor in the predictive models.…”

Section: Introductionmentioning

confidence: 99%

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The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms

Zhang

Liu

et al. 2022

Front. Psychiatry

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BackgroundEarly to identify male schizophrenia patients with violence is important for the performance of targeted measures and closer monitoring, but it is difficult to use conventional risk factors. This study is aimed to employ machine learning (ML) algorithms combined with routine data to predict violent behavior among male schizophrenia patients. Moreover, the identified best model might be utilized to calculate the probability of an individual committing violence.MethodWe enrolled a total of 397 male schizophrenia patients and randomly stratified them into the training set and the testing set, in a 7:3 ratio. We used eight ML algorithms to develop the predictive models. The main variables as input features selected by the least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) were integrated into prediction models for violence among male schizophrenia patients. In the training set, 10 × 10-fold cross-validation was conducted to adjust the parameters. In the testing set, we evaluated and compared the predictive performance of eight ML algorithms in terms of area under the curve (AUC) for the receiver operating characteristic curve.ResultOur results showed the prevalence of violence among male schizophrenia patients was 36.8%. The LASSO and LR identified main risk factors for violent behavior in patients with schizophrenia integrated into the predictive models, including lower education level [0.556 (0.378–0.816)], having cigarette smoking [2.121 (1.191–3.779)], higher positive syndrome [1.016 (1.002–1.031)] and higher social disability screening schedule (SDSS) [1.081 (1.026–1.139)]. The Neural Net (nnet) with an AUC of 0.6673 (0.5599–0.7748) had better prediction ability than that of other algorithms.ConclusionML algorithms are useful in early identifying male schizophrenia patients with violence and helping clinicians take preventive measures.

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Section: Discussionsupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

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The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms

Zhang

Liu

et al. 2022

Front. Psychiatry

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“…Cross-sectional evaluations were carried out in this article to classify demographic, clinical, and socio-cultural variables. There have also been several computational methods being used forecast physical aggression in previous events, including 28 predictors [32]. In order to test patients for IPV and injury, this study introduced machine learning models.…”

Section: Related Workmentioning

confidence: 99%

Prediction on Domestic Violence in Bangladesh during the COVID-19 Outbreak Using Machine Learning Methods

Hossain¹,

Asadullah²,

Rahaman³

et al. 2021

Preprint

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In Southern Asia, Bangladesh is a well-known developing country. Because of COVID-19, we continuously face challenges. Not only can these issues occur beyond economic or health concerns, but they also generate dangerous social problems, such as family abuse. Since the inception of this epidemic, multiple social crimes are looming. Remaining home during the lockout period enhances divorce rates. This research presents a customized forecast of family violence during the COVID-19 outbreak by using machine learning methods. In this paper, we have applied Random Forest, Logistic Regression, and Naive Bayes machine learning classifiers to predict family violence and discovered the feature importance. The performance of the classifiers is evaluated based on accuracy, precision, recall, and F-score. We have employed an oversampling strategy named synthetic minority oversampling technique (SMOTE) to solve the imbalance problem of our data. Even, we have tried to compare three machine learning model performances before and after balancing of normalization data. Finally, ROC analyses and confusion matrices were developed and analyzed by using data augmentation. Our proposed system with the random forest classifier performed better with 77% accuracy in comparison with other two machine learning classifiers.

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“…The authors also aimed for machine learning approaches that understood concealed and dynamic data trends and regularities. The review by [22] aimed to establish a predictive method that is clinically applicable. However, the main focus of our research is that we used machine learning approaches to detect domestic abuse during the COVID-19 outbreak while taking into account oversampling (SMOTE) difficulties.…”

Section: Related Workmentioning

confidence: 99%

Prediction on Domestic Violence in Bangladesh during the COVID-19 Outbreak Using Machine Learning Methods

Hossain

Asadullah

Rahaman

et al. 2021

ASI

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The COVID-19 outbreak resulted in preventative measures and restrictions for Bangladesh during the summer of 2020—these unstable and stressful times led to multiple social problems (e.g., domestic violence and divorce). Globally, researchers, policymakers, governments, and civil societies have been concerned about the increase in domestic violence against women and children during the ongoing COVID-19 pandemic. In Bangladesh, domestic violence against women and children has increased during the COVID-19 pandemic. In this article, we investigated family violence among 511 families during the COVID-19 outbreak. Participants were given questionnaires to answer, for a period of over ten days; we predicted family violence using a machine learning-based model. To predict domestic violence from our data set, we applied random forest, logistic regression, and Naive Bayes machine learning algorithms to our model. We employed an oversampling strategy named the Synthetic Minority Oversampling Technique (SMOTE) and the chi-squared statistical test to, respectively, solve the imbalance problem and discover the feature importance of our data set. The performances of the machine learning algorithms were evaluated based on accuracy, precision, recall, and F-score criteria. Finally, the receiver operating characteristic (ROC) and confusion matrices were developed and analyzed for three algorithms. On average, our model, with the random forest, logistic regression, and Naive Bayes algorithms, predicted family violence with 77%, 69%, and 62% accuracy for our data set. The findings of this study indicate that domestic violence has increased and is highly related to two features: family income level during the COVID-19 pandemic and education level of the family members.

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Prediction of physical violence in schizophrenia with machine learning algorithms

Cited by 26 publications

References 43 publications

The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms

The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms

Prediction on Domestic Violence in Bangladesh during the COVID-19 Outbreak Using Machine Learning Methods

Prediction on Domestic Violence in Bangladesh during the COVID-19 Outbreak Using Machine Learning Methods

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