Early Childhood Anemia in Ghana: Prevalence and Predictors Using Machine Learning Techniques
<b>Background/Objectives</b>: Early childhood anemia is a severe public health concern and the most common blood disorder worldwide, especially in emerging countries. This study examines the sources of childhood anemia in Ghana through various societal, parental, and child characteristic...
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| भाषा: | अंग्रेज़ी |
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MDPI AG
2025-07-01
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| श्रृंखला: | Children |
| विषय: | |
| ऑनलाइन पहुंच: | https://www.mdpi.com/2227-9067/12/7/924 |
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| _version_ | 1839616240175284224 |
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| author | Maryam Siddiqa Gulzar Shah Mahnoor Shahid Butt Asifa Kamal Samuel T. Opoku |
| author_facet | Maryam Siddiqa Gulzar Shah Mahnoor Shahid Butt Asifa Kamal Samuel T. Opoku |
| author_sort | Maryam Siddiqa |
| collection | DOAJ |
| description | <b>Background/Objectives</b>: Early childhood anemia is a severe public health concern and the most common blood disorder worldwide, especially in emerging countries. This study examines the sources of childhood anemia in Ghana through various societal, parental, and child characteristics. <b>Methods</b>: This research used data from the 2022 Ghana Demographic and Health Survey (GDHS-2022), which comprised 9353 children. Using STATA 13 and R 4.4.2 software, we analyzed maternal, social, and child factors using a model-building procedure, logistic regression analysis, and machine learning (ML) algorithms. The analyses comprised machine learning methods including decision trees, K-nearest neighbor (KNN), logistic regression, and random forest (RF). We used discrimination and calibration parameters to evaluate the performance of each machine learning algorithm. <b>Results</b>: Key predictors of childhood anemia are the father’s education, socioeconomic status, iron intake during pregnancy, the mother’s education, and the baby’s postnatal checkup within two months. With accuracy (94.74%), sensitivity (82.5%), specificity (50.78%), and AUC (86.62%), the random forest model was proven to be the most effective machine learning predictive model. The logistic regression model appeared second with accuracy (67.35%), sensitivity (76.16%), specificity (56.05%), and AUC (72.47%). <b>Conclusions</b>: Machine learning can accurately predict childhood anemia based on child and paternal characteristics. Focused interventions to enhance maternal health, parental education, and family economic status could reduce the prevalence of early childhood anemia and improve long-term pediatric health in Ghana. Early intervention and identifying high-risk youngsters may be made easier with the application of machine learning techniques, which will eventually lead to a healthier generation in the future. |
| format | Article |
| id | doaj-art-0abe3f19df9f4308a76bbc4ca26ed93a |
| institution | Matheson Library |
| issn | 2227-9067 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Children |
| spelling | doaj-art-0abe3f19df9f4308a76bbc4ca26ed93a2025-07-25T13:18:28ZengMDPI AGChildren2227-90672025-07-0112792410.3390/children12070924Early Childhood Anemia in Ghana: Prevalence and Predictors Using Machine Learning TechniquesMaryam Siddiqa0Gulzar Shah1Mahnoor Shahid Butt2Asifa Kamal3Samuel T. Opoku4Department of Mathematics & Statistics, International Islamic University Islamabad, Islamabad 44000, PakistanDepartment of Health Policy and Management, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USADepartment of Mathematics & Statistics, International Islamic University Islamabad, Islamabad 44000, PakistanDepartment of Statistics, Lahore College for Women University, Lahore 54000, PakistanDepartment of Health Policy and Management, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA<b>Background/Objectives</b>: Early childhood anemia is a severe public health concern and the most common blood disorder worldwide, especially in emerging countries. This study examines the sources of childhood anemia in Ghana through various societal, parental, and child characteristics. <b>Methods</b>: This research used data from the 2022 Ghana Demographic and Health Survey (GDHS-2022), which comprised 9353 children. Using STATA 13 and R 4.4.2 software, we analyzed maternal, social, and child factors using a model-building procedure, logistic regression analysis, and machine learning (ML) algorithms. The analyses comprised machine learning methods including decision trees, K-nearest neighbor (KNN), logistic regression, and random forest (RF). We used discrimination and calibration parameters to evaluate the performance of each machine learning algorithm. <b>Results</b>: Key predictors of childhood anemia are the father’s education, socioeconomic status, iron intake during pregnancy, the mother’s education, and the baby’s postnatal checkup within two months. With accuracy (94.74%), sensitivity (82.5%), specificity (50.78%), and AUC (86.62%), the random forest model was proven to be the most effective machine learning predictive model. The logistic regression model appeared second with accuracy (67.35%), sensitivity (76.16%), specificity (56.05%), and AUC (72.47%). <b>Conclusions</b>: Machine learning can accurately predict childhood anemia based on child and paternal characteristics. Focused interventions to enhance maternal health, parental education, and family economic status could reduce the prevalence of early childhood anemia and improve long-term pediatric health in Ghana. Early intervention and identifying high-risk youngsters may be made easier with the application of machine learning techniques, which will eventually lead to a healthier generation in the future.https://www.mdpi.com/2227-9067/12/7/924anemiachildrenmachine learning |
| spellingShingle | Maryam Siddiqa Gulzar Shah Mahnoor Shahid Butt Asifa Kamal Samuel T. Opoku Early Childhood Anemia in Ghana: Prevalence and Predictors Using Machine Learning Techniques Children anemia children machine learning |
| title | Early Childhood Anemia in Ghana: Prevalence and Predictors Using Machine Learning Techniques |
| title_full | Early Childhood Anemia in Ghana: Prevalence and Predictors Using Machine Learning Techniques |
| title_fullStr | Early Childhood Anemia in Ghana: Prevalence and Predictors Using Machine Learning Techniques |
| title_full_unstemmed | Early Childhood Anemia in Ghana: Prevalence and Predictors Using Machine Learning Techniques |
| title_short | Early Childhood Anemia in Ghana: Prevalence and Predictors Using Machine Learning Techniques |
| title_sort | early childhood anemia in ghana prevalence and predictors using machine learning techniques |
| topic | anemia children machine learning |
| url | https://www.mdpi.com/2227-9067/12/7/924 |
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