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The problem
Intervention: Web-based information system using K-Nearest Neighbor data mining algorithms to classify and monitor children's nutritional status
Who it was tested with: Children and health center staff at Pukesmas Muara Satu, North Aceh District.
Experience from people who deliver programmes: stories, case studies and lessons learned. Valuable for understanding how and why, and for spotting what to test next, but not a measure of effect.
"the results of the KNN (K-Nearest Neighbor) model classification with the recapitulation of the value of new cases with old cases in the first test section is 0.6944, the second test is 0.6388, the third test is 0.555, the fifth test is 0.6388." The study reports algorithm performance metrics from testing the classification model but does not evaluate whether implementation actually improved child nutrition monitoring or health outcomes in practice.
The study presents model performance metrics only; it does not demonstrate whether the system improved actual child health outcomes, staff efficiency, or timeliness of interventions. No comparison group or baseline data is provided.
Across countries and designs
1 study · 1 country
