Abstract
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.
Highlights
IntroductionThe Black Death—which first spread across Europe from 1347 to 1351—had a higher mortality rate than COVID-19, resulting in the deaths of 75–200 million people in Eurasia and North Africa, there are some parallels between the pandemics, including changes in society
We proposed the machine learning (ML) algorithm-based model for predicting domestic violence in Bangladesh during the COVID-19 pandemic
For the imbalanced data, we observed that the accuracy of the domestic violence prediction of our model for the random forest (RF), Logistic regression (LR), and Naive Bayes (NB) algorithms is 64%, 63%, and 58%, respectively
Summary
The Black Death—which first spread across Europe from 1347 to 1351—had a higher mortality rate than COVID-19, resulting in the deaths of 75–200 million people in Eurasia and North Africa, there are some parallels between the pandemics, including changes in society. COVID-19 first broke out in Wuhan, China, in December 2019, and has since spread throughout the world, causing an increase in global fatalities. Violence can be defined as a form of abuse/mistreatment that a family member experiences from another family member. It involves the establishment of control and fear in a relationship through violence and other forms of abuse. Psychological abuse, social abuse, financial abuse, and sexual assault are all examples of family violence. There is no simple concept in the literature that is able to provide valuable guidance for clinicians who treat family violence survivors [3]
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