On the interpretability of the SVM model for predicting infant mortality in Bangladesh.

Background: Although machine learning (ML) models are well-liked for their outperformance in prediction, greatly avoided due to the lack of intuition and explanation of their predictions. Interpretable ML is, therefore, an emerging research field that combines the performance and interpretability of ML models to create comprehensive solutions for complex decision-making analysis. Conversely, infant mortality is a global public health concern affecting health, social well-being, socio-economic development, and healthcare services.

Assessing acceptance and effects of child feeding counselling on nutritional status of children aged 6-23 months in a semi-urban community.

In Bangladesh, only 34 % of the children aged 18-23 months old are given minimum acceptable diets of complementary foods. Objective of the study was to find the effects of complementary feeding counselling on nutritional status among 6-23 months old children of poor families. This was a community-based randomised control trial.

An assessment of random forest technique using simulation study: illustration with infant mortality in Bangladesh.

We aimed to assess different machine learning techniques for predicting infant mortality (<1 year) in Bangladesh. The decision tree (DT), random forest (RF), support vector machine (SVM) and logistic regression (LR) approaches were evaluated through accuracy, sensitivity, specificity, precision, F1-score, receiver operating characteristics curve and -fold cross-validation via simulations. The Boruta algorithm and chi-square ( ) test were used for features selection of infant mortality.

Perceived stress, eating behavior, and overweight and obesity among urban adolescents.

Background: WHO estimated 20% of adolescents (10-19 years) have mental health problems. We examined the prevalence and associated risk predictors of overweight/obesity and perceived stress using eating behaviors and physical activity among school-and-college-going urban adolescents in Bangladesh.

Methods: A cross-sectional study with a multistage sampling technique was employed to select 4609 adolescent students, aged 13-19 years, from all eight Bangladesh divisions during January-June 2019.

Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms.

Background: Stress-related mental health problems are one of the most common causes of the burden in university students worldwide. Many studies have been conducted to predict the prevalence of stress among university students, however most of these analyses were predominantly performed using the basic logistic regression (LR) model. As an alternative, we used the advanced machine learning (ML) approaches for detecting significant risk factors and to predict the prevalence of stress among Bangladeshi university students.

Factors associated with coverage of vitamin a supplementation among Bangladeshi children: mixed modelling approach.

Background: Vitamin A deficiency (VAD) is a prominent and widespread public health problem in developing countries, including Bangladesh. About 2% of all deaths among under-five children are attributable to VAD. Evidence-based information is required to understand the influential factors to increase vitamin A supplementation (VAS) coverage and reduce VAD.