Development of a Maternal Health System for Remote Areas

Macrohon, Julio Jerison E.; Balan, Ariel Kelly D.; Fuentes, Gloren S.; Goma, Joel C. De

doi:10.1145/3374549.3374575

Cited by 1 publication

(2 citation statements)

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“…With this approach, our goal was to answer the data gap and to apply it to something important and worthwhile, such as high-risk pregnancies, which can affect even maternal mortality. As the Philippines is a middle-income country without many resources, using a simpler, easier, and affordable option such as this model can be significant, as it can be deployed to low-income or even geographically isolated and disadvantaged areas (GIDAs) [ 5 ].…”

Section: Discussionmentioning

confidence: 99%

“…The purpose of this study was to (1) make use of the data collected from local health centers and predict the risk of pregnancy complications; (2) make use of the prediction to fully prepare not just the mother but also the community in the successful delivery of the child [ 5 ]; and (3) leverage the use of semi-supervised approach in boosting the accuracy of supervised machine learning models.…”

Section: Introductionmentioning

confidence: 99%

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A Semi-Supervised Machine Learning Approach in Predicting High-Risk Pregnancies in the Philippines

et al. 2022

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Early risk tagging is crucial in maternal health, especially because it threatens both the mother and the long-term development of the baby. By tagging high-risk pregnancies, mothers would be given extra care before, during, and after pregnancies, thus reducing the risk of complications. In the Philippines, where the fertility rate is high, especially among the youth, awareness of risks can significantly contribute to the overall outcome of the pregnancy and, to an extent, the Maternal mortality rate. Although supervised machine learning models have ubiquity as predictors, there is a gap when data are weak or scarce. Using limited collected data from the municipality of Daraga in Albay, the study first compared multiple supervised machine learning algorithms to analyze and accurately predict high-risk pregnancies. Through hyperparameter tuning, supervised learning algorithms such as Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and Multilayer Perceptron were evaluated by using 10-fold cross validation to obtain the best parameters with the best scores. The results show that Decision Tree bested other algorithms and attained a test score of 93.70%. To address the gap, a semi-supervised approach using a Self-Training model was applied to the modified Decision Tree, which was then used as the base estimator with a 30% unlabeled dataset and achieved a 97.01% accuracy rate which outweighs similar studies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%