CLASSIFICATION OF TODDLER NUTRITIONAL STATUS USING SUPPORT VECTOR MACHINE AND RANDOM FOREST TECHNIQUES WITH OPTIMAL FEATURE SELECTION

Femmi Widyawati; Hanif Pandu Suhito; Warusia  Yassin; Heru Agus Santoso

doi:10.52436/1.jutif.2024.5.6.4162

Femmi Widyawati Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Hanif Pandu Suhito Semarang City Health Office, Indonesia
Warusia Yassin Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Malaysia
Heru Agus Santoso Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.6.4162

Keywords: classification, nutritional status, prediction, random forest, support vector machine

Abstract

Nutritional problems in toddlers, such as stunting, wasting, being underweight, and obesity, are major challenges in monitoring toddler health in Indonesia because they can hurt toddler growth and development. Therefore, handling nutritional problems comprehensively, including prevention efforts and appropriate dietary interventions, is very important. This study aims to develop a toddler nutritional status classification model based on machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest, by utilizing a toddler dataset obtained from Health Institutions in Indonesia containing 9,735 data. The model was designed using the Recursive Feature Elimination (RFE) technique for selecting relevant features and the Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalance. The results showed that the Random Forest algorithm performed best with 95% accuracy, 77% precision, 87% recall, and 81% f1-score. This study contributes to developing a machine learning-based approach to support a more effective nutritional monitoring system and enable more appropriate dietary interventions to address toddler health problems in Indonesia.

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References

C. L. Nelms et al., “Assessment of nutritional status in children with kidney diseases — clinical practice recommendations from the Pediatric Renal Nutrition Taskforce,” pp. 995–1010, 2021, doi: https://doi.org/10.1007/s00467-020-04852-5.

Kementerian Kesehatan Indonesia, Menuju Solusi Gizi Seimbang: Tantangan dan Langkah-langkah Konkrit di Indonesia. 2021.

F. I. Shiyam et al., “Relationship Between Mother ’ s Knowledge Level In Utilizing The Maternal and Child Health Book With The Nutritional Status Of Toddlers,” no. 3, 2024, doi: https://doi.org/10.62951/ijph.v1i3.72.

B. Kebijakan, P. Kesehatan, and K. K. Ri, Status Gizi SSGI 2022.

A. Soliman et al., “Early and long-term consequences of nutritional stunting: From childhood to adulthood,” Acta Biomedica, vol. 92, no. 1, 2021, doi: 10.23750/abm.v92i1.11346.

D. Kurniasari, R. Nurul Hidayah, and R. Khoirun Nisa, “CLASSIFICATION MODELS FOR ACADEMIC PERFORMANCE: A COMPARATIVE STUDY OF NAÏVE BAYES AND RANDOM FOREST ALGORITHMS IN ANALYZING UNIVERSITY OF LAMPUNG STUDENT GRADES,” Jurnal Teknik Informatika (JUTIF), vol. 5, no. 5, pp. 1267–1276, 2024, doi: 10.52436/1.jutif.2024.5.5.2066.

J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing, vol. 408, 2020, doi: 10.1016/j.neucom.2019.10.118.

M. N. Injadat, A. Moubayed, A. B. Nassif, and A. Shami, “Machine learning towards intelligent systems: applications, challenges, and opportunities,” Artif Intell Rev, vol. 54, no. 5, 2021, doi: 10.1007/s10462-020-09948-w.

I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” 2021. doi: 10.1007/s42979-021-00592-x.

A. D. Milan Picard, Marie-Pier Scott-Boyer, Antoine Bodein, Olivier Périn, “Integration strategies of multi-omics data for machine learning analysis,” Comput Struct Biotechnol J, vol. 19, pp. 3735–3746, 2021, doi: https://doi.org/10.1016/j.csbj.2021.06.030.

G. A. F. Khansa and P. H. Gunawan, “Predicting Stunting in Toddlers Using KNN and Naïve Bayes Methods,” International Conference on Data Science and Its Applications (ICoDSA), pp. 17–21, 2024, doi: 10.1109/ICoDSA62899.2024.10651676.

G. P. I. I. Y. Sri Rahmawati, “Implementation of KNN and ANN to the classification of the nutritional status of toddlers based on anthropometric indices,” CoSciTech, vol. 4, no. 2, pp. 385–393, Aug. 2023, doi: https://doi.org/10.37859/coscitech.v4i2.5079.

E. O. Abiodun, A. Alabdulatif, O. I. Abiodun, M. Alawida, A. Alabdulatif, and R. S. Alkhawaldeh, “A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities,” 2021. doi: https://doi.org/10.1007/s00521-021-06406-8.

J. P. Li, A. U. Haq, S. U. Din, J. Khan, A. Khan, and A. Saboor, “Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.3001149.

A. Sakho, E. Scornet, and E. Malherbe, “Theoretical and experimental study of SMOTE: limitations and comparisons of rebalancing strategies,” 2024. [Online]. Available: https://hal.science/hal-04438941v1

N. Cesario, D. Lewis, C. Rosales, F. Antolini, R. Stojanovic, and L. Vandenberg, “Ransomware Detection Using Opcode Sequences and Machine Learning: A Novel Approach with t-SNE and Support Vector Machines,” Oct. 22, 2024. doi: 10.36227/techrxiv.172963142.20817264/v1.

J. Quist, L. Taylor, J. Staaf, and A. Grigoriadis, “Random forest modelling of high-dimensional mixed-type data for breast cancer classification,” Cancers (Basel), vol. 13, no. 5, 2021, doi: 10.3390/cancers13050991.

B. E. , Yeboah, J. Otoo, and D. A. Abaye, “Basic Tenets of Classification Algorithms K -Nearest-Neighbor , Support Vector Machine , Random Forest and Neural Network : A Review,” Journal of Data Analysis and Information Processing, vol. 8, no. 4, 2020, doi: https://doi.org/10.4236/jdaip.2020.84020.

M. K. Dahouda and I. Joe, “A Deep-Learned Embedding Technique for Categorical Features Encoding,” IEEE Access, vol. 9, 2021, doi: 10.1109/ACCESS.2021.3104357.

P. Ghosh et al., “Efficient prediction of cardiovascular disease using machine learning algorithms with relief and lasso feature selection techniques,” IEEE Access, vol. 9, 2021, doi: 10.1109/ACCESS.2021.3053759.

M. G. Karthik, “Detecting Internet of Things Attacks Using Post Pruning Decision Tree-Synthetic Minority Over Sampling Technique,” International Journal of intelligent Engineering & System, vol. 14, no. 4, pp. 105–114, 2021, doi: 10.22266/ijies2021.0831.10.

R. C. Chen, C. Dewi, S. W. Huang, and R. E. Caraka, “Selecting critical features for data classification based on machine learning methods,” J Big Data, vol. 7, no. 1, 2020, doi: 10.1186/s40537-020-00327-4.

J. H. Joloudari, M. Abdolreza, M. Ali Nematollahi, and S. Sunday Oyelere, “Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks,” Applied Sciences, vol. 13, no. 6, 2023, doi: https://doi.org/10.3390/app13064006.

J. Brandt and E. Lanzén, “A Comparative Review of SMOTE and ADASYN in Imbalanced Data Classification,” Dissertation, 2021, [Online]. Available: https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1519153

X. Wu, C. S. Lai, C. Bai, L. L. Lai, Q. Zhang, and B. Liu, “Optimal kernel ELM and variational mode decomposition for probabilistic PV power prediction,” Energies (Basel), vol. 13, no. 14, 2020, doi: 10.3390/en13143592.

D. Yates and M. Z. Islam, “FastForest: Increasing random forest processing speed while maintaining accuracy,” Inf Sci (N Y), vol. 557, pp. 130–152, 2021, doi: https://doi.org/10.1016/j.ins.2020.12.067.

D. R. Anamisa, A. Jauhari, and F. A. Mufarroha, “PERFORMANCE TEST OF NAIVE BAYES AND SVM METHODS ON CLASSIFICATION OF MALNUTRITION STATUS IN CHILDREN,” Communications in Mathematical Biology and Neuroscience, vol. 2024, 2024, doi: 10.28919/cmbn/8429..