Prediction of linear model on stunting prevalence with machine learning approach

Abstract

An increase in the number of residents should be anticipated including in the health sector, especially the problem of stunting. Stunting in children disrupts height and lack of absorption of nutrients. Information and data drive change in many areas such as health, entertainment, economics, business, and other strategic areas. The stages carried out in this study are initiating, developing linear models, and making prediction results on linear machine learning models. The results of testing with the scikit-learn linear model with a minimum variable of 19 get the best test results, namely the polynomial regression with pipeline model with mean absolute percentage error (MAPE) 0.02, root mean square error (RMSE) 3.32, and coefficient of determination (R2) 1,00. Testing with the scikit-learn linear model with a maximum variable of 48 gets the best test results, namely the polynomial regression with pipeline model with MAPE 0.00, RMSE 3.79 and R2 1.00. Testing with the scikit-learn linear model with an average variable of 32 gets the best test results, namely the polynomial regression model with MAPE 0.01, RMSE 3.32, and R2 1.00. The results of testing with the scikit-learn linear model with the minimum, maximum, and average variables get the best test results, namely the polynomial regression with pipeline model.

Analisis Perbandingan Kinerja Model Machine Learning untuk Memprediksi Risiko Stunting pada Pertumbuhan Anak

“…Teknik ini melibatkan penggunaan algoritma dan model statistik untuk mengidentifikasi pola dalam data dan membuat prediksi atau keputusan tanpa instruksi yang eksplisit. Implementasi ML telah banyak di terapkan untuk melakukan prediksi dalam berbagai kasus [13], [14]. Pada kasus stunting, beberapa metode ML yang telah digunaakan adalah metode DT [15], Naïve Bayes [16], [17], SVM [18], k-Nearest Neighbors [19], Neural Networks [20], [21], Random Forest [22], [23] dan Logistic Regression [24] [25].…”

Shannon entropy on near-infrared spectroscopy for nondestructively determining water content in oil palm

“…Regression is a method of estimating relationships from given data to describe the characteristic of a data set [27]. This relationship can then be used for various calculations such as for forecasting future values [28]. In polynomial regression, the correlation between the explanatory and response variables is represented by a polynomial of the appropriate degree.…”

Unlocking potential of data: A localized data-driven approach for stunting reduction in South Kalimantan Province

“…Furthermore, Febrina et al identified nutritional factors such as low birth weight in infants, chronic energy deficiency in pregnant women, and insufficient complementary feeding alongside breastfeeding as contributors to stunting [11]. Meanwhile, studies on environmental factors such as sanitation and clean water access have produced differing conclusions [12,13]. Drawing on these insights, this study investigates the roles of social, governmental, nutritional, and environmental factors in stunting prevalence within districts and cities by utilising a wide array of variables.…”