Summary of Risk Factor Identification and Classification Of Malnutrition Among Under-five Children in Bangladesh: Machine Learning and Statistical Approach, by Tasfin Mahmud et al.
Risk factor identification and classification of malnutrition among under-five children in Bangladesh: Machine learning and statistical approach
by Tasfin Mahmud, Tayab Uddin Wara, Chironjeet Das Joy
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study aims to understand the factors contributing to under-five children’s malnutrition using machine learning algorithms. Four well-established models – Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP) neural network – were used to classify different malnutrition stages. The performance of each model was evaluated using accuracy, precision, recall, and F1 scores. Statistical analysis showed significant correlations between malnutrition and factors such as weight for age Z score, breastfeeding, diarrhea, and wealth index. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special computer programs (machine learning algorithms) to understand why some children are not getting enough food or nutrients. They looked at data from all over the country and found four good ways to group together the reasons why this is happening. The best way was using a “Random Forest” model, which got almost 99% of the answers correct! They also found that factors like how much weight a child has compared to their height, whether they’re still breastfeeding, and how sick they’ve been recently all have an impact on whether or not they’re malnourished. |
Keywords
» Artificial intelligence » Decision tree » Machine learning » Neural network » Precision » Random forest » Recall » Support vector machine
