Supervised models to predict the Stunting in East Aceh

Abstract

Nowadays, Undernutrition is the main cause of child death in developing countries. There are many people and organizations try to mitigate or minimize case of child death. Thus, this paper aimed to has excellent method to handle undernutrition case by exploring the efficacy of machine learning (ML) approaches to predict Stunting in East Aceh administrative zones of Indonesia and to identify the most important predictors. The study employed ML techniques using retrospective cross-sectional survey data from East Aceh, a national-representative data is collected from government by using 2019 about stunting data. We explored Random forest commonly used ML algorithms. Random Forest (RF) as an extension of bagging that in addition for taking random sample of data and also uses random subset of features which mitigates over fitting. Our results showed that the considered machine learning classification algorithms by random forest can effectively predict the stunting status in East Aceh administrative zones. Persistent stunting status was found in the east part of Aceh. The identification of high-risk zones can provide more useful information and data to decision-makers for trying to reduce child undernutrition.

Analysis of Delivery Data by Medical Staff Using The K-Means Algorithm in Sleman District

“…Pregnant women should check their pregnancies regularly until the first 1000 days, the period from when the child is in the womb until the child is two years old [1]. Problems during this period can be dealt with immediately so that the baby's health is maintained and stunting can be overcome immediately [2], [3]. Giving birth to inadequate health facilities and assistance from experts can help mothers, and babies get the best treatment they need.…”