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Background: Low birth weight (LBW) is a critical global health issue that affects infants disproportionately, particularly in developing countries. This study adopted causal machine learning (CML) algorithms for predicting LBW in newborns, drawing from midwife-led continuity care (MLCC). Methods: A quasi-experimental study was carried out in the North Shoa Zone of Ethiopia from August 2019 to September 2020. A total of 1166 women were allocated into two groups. The first group, the MLCC group, received all their antenatal, labor, birth, and immediate post-natal care from a single midwife. The second group received care from various staff members at different times throughout their pregnancy and childbirth. In this study, CML was implemented to predict LBW. Data preprocessing, including data cleaning, was conducted. CML was then employed to identify the most suitable classifier for predicting LBW. Gradient boosting algorithms were used to estimate the causal effect of MLCC on LBW. Moreover, meta-learner algorithms were utilized to estimate the individual treatment effect (ITE), the average treatment effect (ATE), and performance. Moreover, meta-learner algorithms were utilized to estimate the individual treatment effect (ITE), the average treatment effect (ATE), and performance. Results: The study results revealed that Causal K-Nearest Neighbors (CKNN) was the most effective classifier based on accuracy and estimated LBW using a 94.52% accuracy, 90.25% precision, 92.57% recall, and an F1 score of 88.2%. Meconium aspiration, perinatal mortality, pregnancy-induced hypertension, vacuum babies in need of resuscitation, and previous surgeries on their reproductive organs were identified as the top five features affecting LBW. The estimated impact of MLCC versus other professional groups on LBW was analyzed using gradient boosting algorithms and was found to be 0.237. The estimated ATE for the S-learner was 0.284, which is lower than the true ATE of 0.216. Additionally, the estimated ITE for both the T-learner and X-learner was less than -0.5, indicating that mothers would not choose to participate in the MLCC program. Conclusions: Based on these findings, the CKNN classifier demonstrated a higher accuracy and effectiveness. The S-learner and R-learner models, utilizing the XGBoost Regressor and BaseSRegressor, provided accurate estimations of ITE for assessing the impact of the MLCC program. Promoting the MLCC program could help stabilize LBW outcomes. [ABSTRACT FROM AUTHOR]

Near infra-red spectroscopy (NIRS) has been suggested as a rapid, cost-effective, and accurate diagnostic tool for leaf nutrient analysis that could replace more traditional laboratory diagnostics. To ease operational workflows, there would advantage in estimating nutrients using a single method, namely NIRS. This study evaluated the potential of NIRS as a diagnostic method for the measurement of key macro and micronutrients in sugarcane leaf samples. Three hundred and fifty-one sugarcane leaf samples used in quality control reference analysis in Fertiliser Advisory Service (FAS) were used for model calibration. About 35% of the samples were from growers within South Africa, while the remainder were from estates across southern and eastern Africa. Dried and milled leaf material was scanned on a Bruker MPA-NIRS instrument, and spectral pre-processing was performed. Support vector machine regression (SVMR) and partial least squares regression (PLSR) were used for calibrating the estimation models with the test validation(Tval) procedure. The results showed both the PLSR and SVMR model resulted in the best calibration for (K, Ca, Mg)(R2 >87% and the ratio of performance to inter-quartile distance(RPIQ) > 2.1). With a test validation dataset, the SVMR yielded relatively high accuracy(R2 >85% and the RPIQ > 2.5, while PLSR yielded poor accuracy. The SVMR also outperforms Zn(R2 = 0.86; and RPIQ = 2.5) for model calibration. NIR Spectrometry combined with the SVMR technique resulted in a practical option to accurately measure leaf nutrient concentrations and evaluate sugarcane mineral contents accurately. These results indicate that NIRS can replace the current analytical methods used in FAS. [ABSTRACT FROM AUTHOR]

Introduction: Numerous natural and man-made factors have afflicted Ethiopia, and millions of people have experienced food insecurity. The current cut-points of the WFP food consumption score (FCS) have limitations in measuring the food insecurity level of different feeding patterns due to the diversified culture of the society. The aim of this study is to adapt the WFP food security score cut-points corrected for the different feeding cultures of the society using effect-driven quantile clustering., Method: The 2012, 2014, and 2016 Ethiopian socio-economic household-based panel data set with a sample size of 3,835 households and 42 variables were used. Longitudinal quantile regression with fixed individual-specific location-shift intercept of the free distribution covariance structure was adopted to identify major indicators that can cluster and level quantiles of the FCS., Result: Household food insecurity is reduced through time across the quintiles of food security score distribution, mainly in the upper quantiles. The leveling based on effect-driven quantile clustering brings 35.5 and 49 as the FCS cut-points corrected for cultural diversity. This corrected FCS brings wider interval for food insecure households with the same interval range for vulnerable households, where the WFP FCS cut-points under estimate it by 7 score. Education level, employment, fertilizer usage, farming type, agricultural package, infrastructure-related factors, and environmental factors are found to be the significant contributing factors to food security. On the other hand, the age of the head of the household, dependency ratio, shock, and no irrigation in households make significant contributions to food insecurity. Moreover, households living in rural areas and farming crops on small lands are comparatively vulnerable and food insecure., Conclusion: Measuring food insecurity in Ethiopia using the WFP FCS cut-off points underestimates households' food insecurity levels. Since the WFP FCS cut-points have universality and comparability limitations, there is a need for a universally accepted local threshold, corrected for local factors those resulted in different consumption patterns in the standardization of food security score. Accordingly, the quantile regression approach adjusts the WFP-FCS cut points by adjusting for local situations. Applying WFP cut-points will wrongly assign households on each level, so the proportion of households will be inflated for the security level and underestimated for the insecure level, and the influence of factors can also be wrongly recommended the food security score for the levels. The quantile clustering approach showed that cropping on a small land size would not bring about food security in Ethiopia. This favors the Ethiopian government initiative called integrated farming "ኩታ ገጠም እርሻ" which Ethiopia needs to develop and implement a system that fits and responds to this technology and infrastructure., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wubetie, Zewotir, Mitku and Dessie.)