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.

In clinical, sciences expectation of heart malady is one of the foremost troublesomeundertakings. Nowadays, coronary illness may be a significant reason for bleakness andmortality in present-day society. Coronary illness could be a term that doles intent on countlessailments identified with the heart. Clinical determination is incredibly a big, however entanglederrand that must be performed precisely, effectively, and unequivocally. Although hugeadvancement has been imagined within the finding and treatment of coronary illness, furtherexamination is required. The accessibility of enormous measures of clinical informationprompts the requirement for amazing information examination instruments to get ridof valuable information. Coronary illness determination is one in all the applications whereinformation mining and AI instruments have demonstrated victories. This study used themachine learning algorithms KNN, Naïve Bayes, Random forest, Logistic regression, Supportvector machine, J48, and Decision tree by WEKA software to spot which method providesmaximum performance and accuracy. Using these algorithms with WEKA software, we madean ensemble (Vote) hybrid model by combining individual methods. Our research aims toaccess the effectiveness of various machine learning algorithms to diagnose the center diseaseand find the feasible algorithm, which is that the best for a heart condition

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.

Diabetes is a cardiovascular disease. It is not only an epidemic in Bangladesh but also in the whole world that is increasing rapidly. At an early period of human life, machine learning techniques are used to predict diabetes datasets. In our research paper, we use the Pima diabetes dataset from the Kaggle UCI machine learning data repository. For diabetic patients and doctors, machine learning techniques are both cost-effective and time-saving. We apply KNN, Nave Bayes, Random forest, Support vector machine, Simple logistic, and J48 to Pima datasets. Besides these algorithms, we may develop an ensemble (Vote) hybrid model with WEKA software by combining individual methods that provide the best performance and accuracy. Also, try to make a comparison among all machine learning tool’s accuracy and performance with the proposed ensemble model.