Prediction of Stunting Prevalence in Toddlers Using Support Vector Machine Algorithm and Synthetic Minority Oversampling Technique (SMOTE)
Main Article Content
Abstract
Stunting in toddlers represents a condition where isa nutritional deficiency. This becomes more complex when there is insufficient information regarding stunting in toddlers available. Predicting the prevalence of stunting in toddlers involves studying a dataset of stunting prevalence among toddlers through a supervised learning model using Support Vector Machine (SVM) and synthetic minority oversampling technique (SMOTE). The use of SMOTE serves as a data balancing method, while exploratory data analysis (EDA) acts as the preprocessing method for the toddler dataset. From the research implementation on a dataset consisting of 6879 toddlers, an accuracy of 94% was obtained for predictions. This accuracy is comprised of 94% accuracy, 95% precision, 94% recall, and a 94% F1-score.
Article Details
Section
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The proposed policy for journals that offer open access
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- Author grant the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
References
Amanda, R., & Negara, E. S. (2020). Analysis and Implementation Machine Learning for YouTube Data Classification by Comparing the Performance of Classification Algorithms. Jurnal Online Informatika, 5(1), 61–72. https://doi.org/10.15575/join.v5i1.505
Austin, R. R., Mathiason, M. A., & Monsen, K. A. (2022). Using data visualization to detect patterns in whole-person health data. Research in Nursing and Health, 45(4), 466–476. https://doi.org/10.1002/nur.22248
Barros, M. T., Siljak, H., Mullen, P., Papadias, C., Hyttinen, J., & Marchetti, N. (2022). Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks. Molecules, 27(19), 1–23. https://doi.org/10.3390/molecules27196256
Bharti, Gill, N. S., & Gulia, P. (2023). Exploring machine learning techniques for fake profile detection in online social networks. International Journal of Electrical and Computer Engineering, 13(3), 2962–2971. https://doi.org/10.11591/ijece.v13i3.pp2962-2971
Booeshaghi, A. S., Sullivan, D. K., & Pachter, L. (2023). Universal preprocessing of single-cell genomics data. BioRxiv, 2023.09.14.543267. https://www.biorxiv.org/content/10.1101/2023.09.14.543267v1%0Ahttps://www.biorxiv.org/content/10.1101/2023.09.14.543267v1.abstract
Doshi, N., Gundam, S., & Chaudhury, B. (2021). Strategizing University Rank Improvement using Interpretable Machine Learning and Data Visualization. http://arxiv.org/abs/2110.09050
Dritsas, E., & Trigka, M. (2023). Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2. Sensors, 23(1). https://doi.org/10.3390/s23010040