September 5, 2025Food Science & NutritionOpen Access

Machine Learning‐Based Prediction of Determinants of Appropriate Complementary Feeding Practices Among Women With Children Aged 6–23 Months in Sub‐Saharan Africa

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Discussion

Analysis uncovers predictors of complementary feeding in children, indicating significant health implications.

The study found appropriate complementary feeding practices are alarmingly low, at just 9.1% among children.
Using a Random Forest model, an accuracy of 91% and an AUC of 96% were achieved, pinpointing crucial feeding determinants.
Data was analyzed from a comprehensive dataset encompassing 24,235 respondents across eight Sub-Saharan African countries.
Key predictors include maternal education, breastfeeding status, and access to healthcare facilities, emphasizing the need for interventions.

Baykemagn et al. (2025) studied this question.

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September 5, 2025Food Science & NutritionOpen Access

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Authors

Analysis uncovers predictors of complementary feeding in children, indicating significant health implications.

The study found appropriate complementary feeding practices are alarmingly low, at just 9.1% among children.
Using a Random Forest model, an accuracy of 91% and an AUC of 96% were achieved, pinpointing crucial feeding determinants.
Data was analyzed from a comprehensive dataset encompassing 24,235 respondents across eight Sub-Saharan African countries.
Key predictors include maternal education, breastfeeding status, and access to healthcare facilities, emphasizing the need for interventions.

Baykemagn et al. (2025) studied this question.

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