A comparative study of machine learning algorithms for predicting domestic violence vulnerability in Liberian women.

Domestic violence against women is a prevalent in Liberia, with nearly half of women reporting physical violence. However, research on the biosocial factors contributing to this issue remains limited. This study aims to predict women's vulnerability to domestic violence using a machine learning approach, leveraging data from the Liberian Demographic and Health Survey (LDHS) conducted in 2019-2020. We employed seven machine learning algorithms to achieve this goal, including ANN, KNN, RF, DT, XGBoost, LightGBM, and CatBoost. Our analysis revealed that the LightGBM and RF models achieved the highest accuracy in predicting women's vulnerability to domestic violence in Liberia, with 81% and 82% accuracy rates, respectively. One of the key features identified across multiple algorithms was the number of people who had experienced emotional violence. These findings offer important insights into the underlying characteristics and risk factors associated with domestic violence against women in Liberia. By utilizing machine learning techniques, we can better predict and understand this complex issue, ultimately contributing to the development of more effective prevention and intervention strategies.

Knowledge, attitude, and practice of university students towards monkeypox in Bangladesh.

The recent outbreak of viral zoonotic disease-monkeypox-caused by the monkeypox virus, has infected many people worldwide. This study aims to explore the knowledge, attitudes, and practices (KAP) concerning monkeypox among university students in Bangladesh. Data were collected using purposive snowball sampling from 887 university students through an online survey using Google Form. The participants were mostly in their twenties (M = 22.33 [SD 2.01] years), and they spent, on average, 2.59 [SD 1.91] hours/day on electronic and social media. The participants generally showed moderate knowledge (39.5%), low attitude (25.1%), and moderate practice (48.6%) toward monkeypox, with 47.6% having had a moderate KAP score. Findings further showed that personal attributes of university students, i.e., age, sex, year of schooling, residence, living status, geographical distribution, e.g., division, were statistically and significantly associated with knowledge, attitudes, and practices regarding monkeypox and overall KAP score. It is also apparent that health status, susceptibility to monkeypox, and exposure to social media were the most common factors significantly associated with knowledge, attitudes, and practices regarding monkeypox and overall KAP score. The current study's findings underscore the need for developing appropriate information, education, and communication (IEC) materials and their dissemination, which could play an important role in reducing the risk of monkeypox and similar other infectious diseases, particularly among students in Bangladesh.

Prevalence and determinants of physical violence against doctors in Bangladeshi tertiary care hospitals.

BACKGROUND: The increasing physical violence against doctors in the health sector has become an alarming global problem and a key concern for the health system in Bangladesh. This study aimed to determine the prevalence and associated factors of physical violence against doctors in Bangladeshi tertiary care hospitals. METHODS: A cross-sectional survey was performed among 406 doctors working in tertiary care hospitals. Data were collected using a self-administered questionnaire and the binary logistic regression model was employed for predicting physical violence against doctors. RESULTS: Of the participants, 50 (12.3%) doctors reported being exposed to physical violence in 12 months prior to the survey. According to logistic regression analysis, aged less than 30 years or younger, male and never-married doctors were prone to physical violence. Similarly, doctors from public hospitals and those worked in emergency departments were at higher risk of physical violence. More than 70% of victims reported that patients' relatives were the main perpetrators. Two-thirds of the victims referred to violence in the hospitals as a grave concern. CONCLUSIONS: Physical violence against doctors is relatively common in the emergency departments and public hospitals in Bangladesh. This study found that male and younger doctors were at high risk of exposing physical violence. To prevent hospital violence, authorities must develop human resources, bolster patient protocol and offer physician training.

A hybrid ensemble approach to accelerate the classification accuracy for predicting malnutrition among under-five children in sub-Saharan African countries.

BACKGROUND: The proper intake of nutrients is essential to the growth and maturation of youngsters. In sub-Saharan Africa, 1 in 7 children dies before age 5 y, and more than a third of these deaths are attributed to malnutrition. The main purpose of this study was to develop a majority voting-based hybrid ensemble (MVBHE) learning model to accelerate the prediction accuracy of malnutrition data of under-five children in sub-Saharan Africa. METHODS: This study used available under-five nutritional secondary data from the Demographic and Health Surveys performed in sub-Saharan African countries. The research used bagging, boosting, and voting algorithms, such as random forest, decision tree, eXtreme Gradient Boosting, and k-nearest neighbors machine learning methods, to generate the MVBHE model. RESULTS: We evaluated the model performances in contrast to each other using different measures, including accuracy, precision, recall, and the F1 score. The results of the experiment showed that the MVBHE model (96%) was better at predicting malnutrition than the random forest (81%), decision tree (60%), eXtreme Gradient Boosting (79%), and k-nearest neighbors (74%). CONCLUSIONS: The random forest algorithm demonstrated the highest prediction accuracy (81%) compared with the decision tree, eXtreme Gradient Boosting, and k-nearest neighbors algorithms. The accuracy was then enhanced to 96% using the MVBHE model. The MVBHE model is recommended by the present study as the best way to predict malnutrition in under-five children.

Exploring the Influencing Factors for Contraceptive Use among Women: A Meta-Analysis of Demographic and Health Survey Data from 18 Developing Countries.

Background: The primary objective of this research was to investigate how socioeconomic and demographic factors influence the usage of contraceptives by women in 18 developing countries. Methods: The study used the latest DHS data from 18 developing countries in order to acquire a broad perspective of contraceptive methods. We applied meta-analysis techniques for 18 developing countries to find out the summary results. Results: The overall summary effect showed that the variable respondent education (OR = 1.39; 95% CI: 1.17 to 1.65), husband education (OR = 1.60; 95% CI: 1.32 to 1.93), type of place of residence (OR = 0.88; 95% CI 0.78 to 0.98), current working status (OR = 1.47; 95% CI 1.30 to 1.66), age of the respondent (OR = 3.41; 95% CI 2.35 to 4.93), breastfeeding status (OR = 1.34; 95% CI 1.11 to 1.62), and desire for more children (OR = 0.53; 95% CI 0.43 to 0.65) were the significant factors for contraceptive utilization in developing countries. Conclusions: According to the findings of this descriptive study, the respondent's age, level of education, and work status were shown to be the most significant factors that influence the usage of contraceptives in developing countries. It is necessary to take reasonable steps in order to increase the rate of utilizing methods of contraception among women of reproductive age who are uneducated, living in rural areas, and unemployed.

Knowledge, Attitudes, and Practices concerning Black Fungus during COVID-19 Pandemic among Students of Bangladesh: An Online-Based Cross-Sectional Survey.

BACKGROUND: Infection with COVID-19 and its control entailing steroids and immunomodulatory medications disrupted normal immune function. The ensuing immunological disorder led to the rise of another infection-Black Fungus (Mucormycosis). However, the spread of Black Fungus can be minimized through proper knowledge, informed attitude, and conscious preventive practice. This study aimed to assess students' knowledge, attitude, and practice (KAP) regarding Black Fungus amid the COVID-19 pandemic in Bangladesh. METHODS: This cross-sectional study was carried out among Bangladeshi students from June to July 2021. Using Google Forms, an e-questionnaire was developed for this web-based survey, and the participants were selected through a snowball sampling approach. RESULTS: Out of the 2009 participants, more than half were female (53.5%), and the majority were at an age between 18 and 25 years (31.5%) and had received higher secondary (HSC) schooling (77.8%), while around 61% resided in urban areas. Findings revealed that most of the students (63.8%) spent around 2 h on electronic and social media to become informed about COVID-19 and Black Fungus. Approximately 33% of the students showed low KAP scores (32.9%), whereas around 26% had high KAP scores. Our results show a significant association between KAP and sex, schooling, living status, residence, and media exposure. CONCLUSION: The knowledge of Black Fungus considerably varies among Bangladeshi students considering the place of residence, age, sex, living arrangement, and media exposure. Policymakers should emphasize awareness among people focusing on the results of this study to increase deterrent attitudes and protective practices to minimize the risks of being infected.

Proportion and associated factors of the utilisation of complementary and alternative medicine exclusively in a hospital in Bangladesh.

BACKGROUND: Complementary and alternative medicine (CAM) has played a critical role in ensuring universal access to basic health care services around the world. In Bangladesh, conventional medicine is a common approach for health care practices, yet, due to Bangladesh's high out-of-pocket payment, millions of people utilise CAM-based healthcare services for illnesses. In Bangladesh, there is a scarcity of data on how CAM is perceived and utilised. The goal of this study was to determine the proportion and correlates of the utilisation of CAM among patients visiting a tertiary level hospital, in Bangladesh. METHODS: A cross-sectional survey involving 1,183 patients who received health care from a hospital in Bangladesh was interviewed for this study. The associated factors on utilising CAM were identified using multivariable logistic regression analysis. RESULTS: Thirty-three percent of patients utilised CAM exclusively to treat their illnesses, whereas the rest utilised conventional medicine before CAM. Young adult patients aged 26 to 45 years (AOR = 6.26, 95% CI:3.24-12.07), patients without education (AOR = 2.99, 1.81-4.93), and married patients (AOR = 1.79, 1.08-2.97) were the most likely to be only CAM users. The most common reasons for using CAM were belief in its effectiveness, less adverse effects, affordability and lower costs. CONCLUSION: In Bangladesh, CAM plays a significant role in health care delivery, with high-levels of patient satisfaction and health benefits. Patients who are older and have a higher level of education are more hesitant to use CAM for their illness, yet CAM has the potential to play a significant role in reducing hospitalisation by providing high reliability and low costs.

Depression and anxiety among university students during the COVID-19 pandemic in Bangladesh: A web-based cross-sectional survey.

The purpose of this study was to investigate the prevalence of depression and anxiety among Bangladeshi university students during the COVID-19 pandemic. It also aimed at identifying the determinants of depression and anxiety. A total of 476 university students living in Bangladesh participated in this cross-sectional web-based survey. A standardized e-questionnaire was generated using the Google Form, and the link was shared through social media-Facebook. The information was analyzed in three consecutive levels, such as univariate, bivariate, and multivariate analysis. Students were experiencing heightened depression and anxiety. Around 15% of the students reportedly had moderately severe depression, whereas 18.1% were severely suffering from anxiety. The binary logistic regression suggests that older students have greater depression (OR = 2.886, 95% CI = 0.961-8.669). It is also evident that students who provided private tuition in the pre-pandemic period had depression (OR = 1.199, 95% CI = 0.736-1.952). It is expected that both the government and universities could work together to fix the academic delays and financial problems to reduce depression and anxiety among university students.