Background and objective Low birth weight is one of the primary causes of child mortality and several diseases of future life in developing countries, especially in Southern Asia. The main objective of this study is to determine the risk factors of low birth weight and predict low birth weight babies based on machine learning algorithms. Materials and methods Low birth weight data has been taken from the Bangladesh Demographic and Health Survey, 2017–18, which had 2351 respondents. The risk factors associated with low birth weight were investigated using binary logistic regression. Two machine learning-based classifiers (logistic regression and decision tree) were adopted to characterize and predict low birth weight. The model performances were evaluated by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve. Results The average percentage of low birth weight in Bangladesh was 16.2%. The respondent’s region, education, wealth index, height, twin child, and alive child were statistically significant risk factors for low birth weight babies. The logistic regression-based classifier performed 87.6% accuracy and 0.59 area under the curve for holdout (90:10) cross-validation, whereas the decision tree performed 85.4% accuracy and 0.55 area under the curve. Conclusions Logistic regression-based classifier provided the most accurate classification of low birth weight babies and has the highest accuracy. This study’s findings indicate the necessity for an efficient, cost-effective, and integrated complementary approach to reduce and correctly predict low birth weight babies in Bangladesh.

Voluntary blood donation (VBD) is the foundation of blood safety and safe transfusion methods. It is vital to boost volunteer donor recruitment and retention to ensure a long-term safe blood transfusion practice, especially among university students. The goal was to evaluate Khulna University students' blood donation knowledge, attitude, and practice (KAP) as well as associated factors. A cross-sectional study was conducted at Khulna University in April 2022. Using simple random sampling (SRS), 400 face-to-face interviews were taken, of which 394 records were used for further analysis. A Chi-square test was used to check the association between KAP toward VBD, and binary logistic regression was applied to identify the association between explanatory and outcome variables. The logistic regression reveals that students with good knowledge about VBD are associated with permanent residence (OR: 1.651; 95% CI: 1.028, 2.650) and education (OR: 1.746; 95% CI: 1.012, 3.014). Favorable attitude toward VBD is associated with gender (OR: 1.818; 95% CI: 1.073, 3.079), division (OR: 3.058; 95% CI:1.241, 7.535) and social media time (OR: 0.068; 95% CI:0.001, 0.876). The practice of VBD is associated with gender (OR: 5.375; 95% CI:3.115, 9.273) and current residence (OR: 0.0397; 95% CI:0.181, 0.869). Efforts should be undertaken to use knowledge and a favorable attitude toward students at the Khulna university to accomplish the aim of 100% VBD in the future.