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All Journal Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Jurnal Kesehatan Masyarakat
Rico Kurniawan
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Education Public Health

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Prediction of Anemia Using Machine Learning Algorithms: Scoping Review Kario, Asrit Jessica; Rico Kurniawan
Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Vol. 7 No. 11: NOVEMBER 2024 - Media Publikasi Promosi Kesehatan Indonesia (MPPKI)
Publisher : Fakultas Kesehatan Masyarakat, Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/mppki.v7i11.6289

Abstract

Introduction: One of the major public health problems is anemia, especially affecting newborn and infant children, adolescent girls, young women, pregnant women, and postpartum women. The cause of anemia is the reduced supply of red blood cells in the human body or the damage or weakening of the structure of red blood cells. One of the preferences of utilizing machine learning is the prediction of results. Objective: The purpose of this study is to compare effective algorithms, related to the origin or source of the data set, data set size, metric evaluation and accuracy and produce predictors in predicting anemia using machine learning. Method: This research uses a scoping review method on 4 databases, namely Scopus, EBSCO, PubMed, and IEEE Xplore from 2019 - 2024 with keywords anemia, algorithms, machine learning, and prediction. The results of screening articles on the Scopus, EBSCO, PubMed, and IEEE Xplore databases obtained 384 articles which were then selected through several stages and obtained 9 articles. Result: The review found that the highest algorithm performance in anemia prediction, namely Penalized Regression (LASSO regression) accuracy above 64%, XGboost accuracy 100% and execution time 0.2404 seconds, Catboost accuracy 97.6%, Random Forest accuracy 95.49% and 72%, J48 algorithm accuracy of 97.7%, Logistic Regression accuracy 66% and AUC 69%, and SVM linear AUC 79.9%. Conclusion: Machine learning can assist in the development of anemia prediction models by exploring large amounts of data and producing precise and fast predictors. The predictors obtained are determined by the selection of algorithms in the study.
Families at Risk of Stunting and the Prevalence of Stunting in Indonesia: An Ecological Study Rico Kurniawan; Lina Widyastuti; Sudibyo Alimoeso; Siti Fathonah; Diaini, Meindy; Muhammad Kodir; Welcy Fine; Okky Assetya Pratiwi; Fadhilah, Hafsah Farah
Jurnal Kesehatan Masyarakat Vol. 21 No. 1 (2025)
Publisher : Universitas Negeri Semarang in collaboration with Ikatan Ahli Kesehatan Masyarakat Indonesia (IAKMI Tingkat Pusat) and Jejaring Nasional Pendidikan Kesehatan (JNPK)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/kemas.v21i1.20978

Abstract

Stunting remains a critical public health challenge in Indonesia, impacting child growth, cognitive development, and long-term productivity. The government has prioritized interventions targeting families at risk of stunting to reduce its prevalence. This study examines the relationship between families at risk of stunting and stunting prevalence in Indonesia by an ecological study design. Data were analyzed at the district/city level using correlation analysis to assess key risk factors. The findings indicate that inadequate access to safe drinking water, poor sanitation, substandard housing, and reproductive health risks among women of reproductive age are significantly correlated with higher stunting prevalence (p<0.05). The correlation coefficients for these factors are 0.14, 0.19, 0.17, and 0.33, respectively. Furthermore, a one percent reduction in families at risk of stunting is associated with a 0.19 percent decrease in stunting prevalence (R² = 16%). These results highlight the need for comprehensive interventions addressing environmental, socio-economic, and maternal health factors. Strengthening policies that improve access to clean water, sanitation, and maternal health services is crucial to accelerating stunting reduction efforts in Indonesia. Prioritizing families at risk can enhance the effectiveness of government strategies in achieving national stunting decrease targets.
Co-Authors Diaini, Meindy Fadhilah, Hafsah Farah Kario, Asrit Jessica Lina Widyastuti Muhammad Kodir Okky Assetya Pratiwi Siti Fathonah Sudibyo Alimoeso Welcy Fine