Stunting Classification in Children's Measurement Data Using Machine Learning Models
Abstract
The study conducted a stunting classification of measurement data for children under 5 years old. The dataset has attributes such as: gender, age, weight (BB), height (TB), weight / height (BBTB), weight / age (BBU), and height / age (TBU). The research uses the CRISP-DM methodology in processing the data. The data were tested on several classification models, namely: logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (KNN), classification and regression trees (CART), nave bayes (NB), support vector machine - linear kernel (SVM-Linear), support vector machine - rbf kernel (SVM-RBF), random forest classifier (RPC), adaboost (ADA), and neural network (MLPC). These models were tested on the dataset to find out the best model in accuracy. The test results show that SVM-RBF produces an accuracy of 78%. SVM-RBF has consistently been at the highest accuracy in several tests. Testing through k-fold cross validation with k=10.
References
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees. Routledge.
Fachrie, M. (2020). Machine Learning for Data Classification in Indonesia Regional Elections Based on Political Parties Support. Jurnal Ilmu Komputer dan Informasi, 13(2), 89-96.
Freund, Y., Schapire, R., & Abe, N. (1999). A short introduction to boosting. Journal-Japanese Society For Artificial Intelligence, 14(771-780), 1612.
Ghosh, A., SahaRay, R., Chakrabarty, S., & Bhadra, S. (2021). Robust generalised quadratic discriminant analysis. Pattern Recognition, 117, 107981.
Howlett, R. J., & Jain, L. C. (2001). Radial basis function networks 2: new advances in design (Vol. 2). Springer Science & Business Media.
Indonesia, M. C. A. (2014). Proyek Kesehatan dan Gizi Berbasis Masyarakat untuk Mengurangi Stunting. Tersedia di http://www. mcaindonesia. go. id/assets/uploads/media/pdf/Factsheet_HN_ID. pdf (diakses 25 Oktober 2018).
Jinjia, W., Shaonan, J., & Yaqian, Z. (2019). Quadratic Discriminant Analysis Based on Graphical Lasso for Activity Recognition. In 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP) (pp. 70-74). IEEE.
Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160(1), 3-24.
McLachlan, G. J. (2005). Discriminant analysis and statistical pattern recognition. John Wiley & Sons.
Peng, C. Y. J., & Lee, K. L. 8c Ingersoll, GM (2021). An introduction to logistic regression analysis and reporting. Thejournal ofEducational Research, 96(1), 3-14.
Schapire, R. E. (1990). The strength of weak learnability. Machine learning, 5(2), 197-227.
Stone, M. (1974). Cross‐validatory choice and assessment of statistical predictions. Journal of the royal statistical society: Series B (Methodological), 36(2), 111-133
WHO. (2021). “Weight-for-length/height,” 2021.
Windeatt, T. (2008). Ensemble MLP classifier design. In Computational Intelligence Paradigms (pp. 133-147). Springer, Berlin, Heidelberg.
Copyright (c) 2022 Journal La Multiapp
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.