###### 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. f +
Footnote †: journal: _Syst. Innov._**2021**, 4,  
and fear in a relationship through violence and other forms of abuse. Physical assault, psychological abuse, social abuse, financial abuse, and sexual assault are all examples of family violence. The frequency of the violence can be occasional or chronic [1]. Suicides, violent events, and female and child torture--as a result of family violence--are typically reported [2]. Family violence has increased during pandemic circumstances. There is no simple concept in the literature that is able to provide valuable guidance for clinicians who treat family violence survivors [3]. By analyzing the recent literature, some studies have discussed the factors behind family abuse. Other studies have projected family violence using distinct verification and authentication methods involving children, older people, and couples. Some studies have clarified the forms in which COVID-19 has increased family violence during the lockdown period. However, a rare number of papers used machine learning techniques. None of the researchers have attempted to identify and forecast family violence through the use of machine learning software. In our study, using machine learning algorithms, we concentrate on predicting how family violence occurs and what the critical factors behind family violence are. Throughout this article, from a description, validation, and accuracy standpoint, we also utilize various advanced analyses. Our objective is to prove that our suggested model represents the used data set correctly, with precision, accuracy, sensitivity, specificity, an F1-score, and area under the curve (AUC). We hope to unveil the variables and explanations for the increase in family violence during the lockdown. We hope this paper helps to remedy these heinous incidents, and people in Bangladesh are able to lead stable lives. * How can we forecast family violence in Bangladesh during the COVID-19 outbreak? * What are the most important reasons for the increase in domestic violence in Bangladesh during the COVID-19 outbreak? * How can we use machine learning (ML) algorithms to measure family violence? * Which ML algorithm is more applicable for measuring family violence?