Objectives: Rwanda reported a stunting rate of 33% in 2020, decreasing from 38% in 2015; however, stunting remains an issue. Globally, child deaths from malnutrition stand at 45%. The best options for the early detection and treatment of stunting should be made a community policy priority, and health services remain an issue. Hence, this research aimed to develop a model for predicting stunting in Rwandan children.Methods: The Rwanda Demographic and Health Survey 2019-2020 was used as secondary data. Stratified 10-fold cross-validation was used, and different machine learning classifiers were trained to predict stunting status. The prediction models were compared using different metrics, and the best model was chosen.Results: The best model was developed with the gradient boosting classifier algorithm, with a training accuracy of 80.49% based on the performance indicators of several models. Based on a confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the model’s ability to classify stunting cases correctly at 79.33%, identify stunted children accurately at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve of 0.89. The model found that the mother’s height, television, the child’s age, province, mother’s education, birth weight, and childbirth size were the most important predictors of stunting status.Conclusions: Therefore, machine-learning techniques may be used in Rwanda to construct an accurate model that can detect the early stages of stunting and offer the best predictive attributes to help prevent and control stunting in under five Rwandan children.

A yield of 26.88% of seed oil was obtained from Jatrophacurcas cultivated in Rwanda. Within 2 hours of reaction, the methyl ester (Biodiesel) was produced at a yield of 85.3% from obtained oil through direct base-catalysed trans-esterification process using methanol and sodium hydroxide as alcohol and catalyst. The proportion of 0.6g of NaOH in 20mL of methanol with 100 mL of Jatropha oil was the best ratio for making the biodiesel. The biodiesel obtained had 85.03% of ester content, 0.878 and 7.891Centistokes (at 20 0 C) of specific gravity and viscosity respectively. The other physico-chemical properties were also characterized.

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Purpose: In Rwanda, childhood stunting is a major public health problem. Earlier studies employed traditional statistical approaches to identify causal factors to stunting, and little is known about the uses and effectiveness of machine learning (ML) algorithms that may identify risk factors for a variety of health conditions based on complex data. Methods: This study examines the usefulness of machine learning algorithms in predicting stunting in children under the age of five using data from the 2020 Rwanda Demographic and Health Survey. Random Forest was utilized for feature selection, and supervised machine learning methods were applied. The confusion matrix and Receiver Operating Characteristics (ROC), which incorporated several metrics, were used to evaluate the performance of algorithms. Additionally, the outperformed model identifies variables that strongly predict stunting in Rwanda. Ultimately, multivariate logistic regression was used. Results: The XGBoost classifier predicts stunting with the lowest misclassification error among the selected ML algorithms, followed by a gradient boosting classifier, random forests, support vector machines, classification trees, and logistic regression with forward-stepwise selection. The 10 most important variables in predicting childhood stunting in Rwanda are breastfeeding start, mother’s height, provinces, possessing television, child size at birth, maternal education, maternal BMI, wealth index, preceding birth interval, and child age. Conclusion: This study contributes to the body of knowledge that confirmed the efficacy of ML for population health research and policy decision-making in a broad range of areas, including defining treatment effects in epidemiological studies and child undernutrition. This study shows that the XGBoost classifer is highly recommended because of the combination of flexibility, scalability,regularization, ensemble approaches, and feature importance that distinguishesXGBoost from gradient-based classifers.

Plants have been used as medicine since time immemorial (Ushimaru et al., 2007). Medicinal plants are essential sources of easily accessible remedies used by traditional healers. Henna leaves are used to cure jaundice, skin diseases, dysentery, arthritis (Sharma et al., 2018). Lawsonia inermis is widely used by Rwandan as cosmetic products and in treatment of different ailments. This study was aimed to investigate the phytochemical screening and in vitro antimicrobial activities of different extracts of L. inermis barks collected from Huye District in Southern Province of Rwanda. The dried and powdered barks were extracted with methanol and cyclohexane by maceration giving 1.627g (10.83%), 0.173.g (1.15%) respectively. The extracts were concentrated for further phytochemical tests and evaluated for antimicrobial activity against Escherichia coli and Staphylococcus aureus using disk diffusion method.The results from phytochemical screening revealed the presence of terpenoids, phenolic compounds, tannins, flavonoids, proteins and saponins. Lawsonia inermis barks displayed antimicrobial activity against both gram negative and gram positive bacterial strains used in the present study. The findings of antimicrobial assay showed that the methanol extract of lawsonia inermis barks with the concentration of 10-1 has an antibacterial activity against gram negative bacteria Escherichia coli with zone of inhibition of 10 mm which is same as, the positive control, penicillin inhibition zone (10 mm) with the same concentration. The antibacterial activity of cyclohexane extract against E. coli showed a smaller inhibition zone of 9 mm for diluted inoculum (10-1). For the same concentrations of extracts and inoculums, the methanol extract inhibited Staphylococcus aureus (gram positive) growth with zone of inhibition of 9 mm, while the antibacterial activity of cyclohexane extract against the same bacteria was 4 mm, which are smaller than penicillin inhibition zone (18 mm). Key words: lawsonia inermis, phytochemical screening, anti-microbial activity, E. coli, S. aureus.