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In the Philippines, one in five school-aged children are affected by undernutrition, increasing their risk of physical and cognitive development. The Department of Education (DepEd) attempts to address this issue by targeting children with low body mass index (BMI) for their school-based feeding program (SBFP). However, challenges like inadequate measuring tools and supervision in low-resource communities have led to large discrepancies in the nutritional status of SBFP beneficiaries and non-beneficiaries. Siy Van et al. [1] addresses the difficulties associated with BMI by using machine learning (ML) to predict undernutrition among school-aged children based on socioeconomic and demographic characteristics, dietary diversity scores, and food insecurity scores. Their study compared several ML algorithms and found that their best performing model in terms of accuracy was a random forest (RF) model. However, the RF model had high sensitivity with low specificity, indicating a bias towards the positive class. This study aims to improve these results by employing oversampling techniques and other ML algorithms that were not used in the study. Using the same data set in [1], this study compares four machine learning algorithms (RF, XGBoost, DNN, and NNRF) to predict undernutrition among school-aged children, managing imbalanced data using three oversampling techniques (SMOTE, Borderline-SMOTE, and ADASYN). Eight independent classification tasks for predicting undernutrition were performed, and results showed that a RF-Borderline model performed the best in terms of Cohen's κ (0.3662), with an accuracy of 71.61%, sensitivity of 71.13%, and a specificity of 73.08%. While RF performed the best overall, XGBoost and NNRF performed better than RF on specific tasks. Notably, incorporating oversampling consistently enhanced model performance. [ABSTRACT FROM AUTHOR]

Pharmaceutical drugs and medicines are essential to modern healthcare, and in the Philippines, public healthcare facilities acquire these through the Philippine Government E-Procurement System (PhilGEPS). Economic theory and existing empirical studies show that pooled procurement can reduce unit prices for certain drugs and medicines, wherein pooled procurement occurs when buyers of the same product consolidate their separate orders into one single order. However, extensive research focusing on price-quantity relationships in the Philippine market is yet to be done. Moreover, the effects of pooled procurement on each drug have yet to be explored. As such, this study determines the extent of price changes in drugs and medicines if pooled procurement were implemented for pharmaceuticals in the Philippines. This was done using pharmaceutical bids across 2010 to 2021 from the PhilGEPS database, after pre-processing steps such as item label corrections using fuzzy match algorithms and price conversions to their equivalents in 2012 in consideration of Philippine peso inflation data. Afterwards, modeling price and quantity data was performed for 273 different medicines using the ordinary least squares (OLS) regression algorithm. The results of the modeling showed that the unit prices of 37 drugs and medicines would decrease by a median average of 18.31% and a mean average of 20.31% should orders be pooled, thereby benefiting from pooled procurement. Moreover, most of these 37 pharmaceuticals fall under the following therapeutic areas: "Anti-Infectives for Systemic Use", "Cardiovascular System", or "Nervous System". Such information may help develop recommendations for policymakers regarding which pharmaceutical orders are most appropriate for pooling in pilot runs of pooled procurement systems. [ABSTRACT FROM AUTHOR]