2022
DOI: 10.1186/s12911-022-01992-6
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Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithm

Abstract: Background More than 115,000 maternal deaths and 591,000 prenatal deaths occurred in the world per year with anemia, the reduction of red blood cells or hemoglobin in the blood. The world health organization divides anemia in pregnancy into mild anemia (Hb 10–10.9 g/dl), moderate anemia (Hb 7.0–9.9 g/dl), and severe anemia (Hb < 7 g/dl). This study aims to predict the level of anemia among pregnant women in the case of Ethiopia using homogeneous ensemble machine learning algorithms. … Show more

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Cited by 20 publications
(9 citation statements)
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“…The approach computes the distance between the feature vectors and their nearest neighbors and does not generate duplicates, instead producing synthetic data points that varied slightly from the actual data points. The k-NN had 100 neighbors as the metric was set to Euclidean with uniform weight [ 7 , 23 ].…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The approach computes the distance between the feature vectors and their nearest neighbors and does not generate duplicates, instead producing synthetic data points that varied slightly from the actual data points. The k-NN had 100 neighbors as the metric was set to Euclidean with uniform weight [ 7 , 23 ].…”
Section: Methodsmentioning
confidence: 99%
“…Fatigue, weakness, dizziness and drowsiness, are some of the symptoms caused by anemia by which children and pregnant females are vulnerable, which vary within a country [ 7 ], with compounded risk of mortality for both mother and child. Iron deficiency anemia has additionally been shown to affect psychological features and physical development in children and reduce productivity in adults [ 8 ].…”
Section: Introductionmentioning
confidence: 99%
“…The k‐NN used 100 neighbors and the metric was set to Euclidean with uniform weight while the optimal instance is k = 2 which was assigned to the nearest neighbor in the class. This method computes the distance between the feature vectors and their nearest neighbors and does not produce duplicates, instead providing synthetic data points that differ slightly from the actual data points 6,41 …”
Section: Methodology and Experimental Designmentioning
confidence: 99%
“…This method computes the distance between the feature vectors and their nearest neighbors and does not produce duplicates, instead providing synthetic data points that differ slightly from the actual data points. 6,41…”
Section: K-nearest Neighbor (K-nn) Algorithmmentioning
confidence: 99%
“…The HP also used the causal region as input and tiny MobileNetv2 as the backbone network to complete the Hb concentration prediction. The loss function was presented in (5)(6)(7).…”
Section: Training Networkmentioning
confidence: 99%