Analisis Model Machine learning dan Imputasi Data untuk Prediksi Tinggi Badan Anak: Systematic Literature Review
DOI:
https://doi.org/10.26418/justin.v14i2.94175Keywords:
machine learning, stunting, imputasi, tinggi badan anak, SLRAbstract
Stunting dan wasting merupakan dua masalah kesehatan utama yang berdampak pada pertumbuhan fisik dan perkembangan kognitif anak secara global. Tinggi badan anak adalah salah satu indikator penting dalam mendeteksi kondisi tersebut, namun pengukuran dan pemantauan tinggi badan secara manual sering kali menemui hambatan, seperti ketersediaan data yang memadai. Seiring berkembangnya teknologi, penerapan model machine learning menjadi alternatif yang mulai banyak digunakan untuk memprediksi tinggi badan anak secara lebih efektif dan efisien. Penelitian ini bertujuan untuk mengidentifikasi tren penggunaan machine learning, pendekatan prediktif yang digunakan, teknik imputasi data yang relevan, serta variabel-variabel yang sering digunakan. Metode Systematic Literature Review (SLR) digunakan untuk mengumpulkan dan menganalisis literatur terkait, dengan strategi pencarian yang disusun berdasarkan kata kunci spesifik dan penyaringan menggunakan pendekatan PRISMA. Hasil kajian terhadap puluhan artikel ilmiah mengungkapkan bahwa model-model ensemble seperti Random Forest, Gradient Boosting, dan Neural Network menunjukkan performa prediktif yang sangat baik dan konsisten di berbagai konteks studi. Selain itu, teknik imputasi data terbukti efektif dalam memperbaiki kualitas data input yang hilang, sehingga meningkatkan akurasi prediksi. Namun, studi ini juga menemukan bahwa integrasi antara metode prediksi dan teknik imputasi dalam satu penelitian masih sangat terbatas, sehingga menjadi celah yang potensial untuk dikembangkan dalam penelitian selanjutnya. Selain itu, penelitian ini mengidentifikasi adanya variasi signifikan dalam pelaporan metrik evaluasi dan penggunaan pendekatan yang berbeda sesuai karakteristik data dan tujuan analisis. Temuan ini memberikan landasan penting untuk pengembangan model prediktif yang lebih holistik, mampu mengakomodasi kompleksitas data biologis dan antropometrik, serta berpotensi mendukung intervensi gizi anak secara lebih tepat dan efektif di masa depan.References
P. S. Rasyid, R. Zakaria, and A. Z. T. Munaf, Remaja dan Stunting. Penerbit Nem, 2022.
M. Majid, A. J. Tharihk, and R. Zarkasyi, Cegah stunting melalui perilaku hidup sehat. Penerbit NEM, 2022.
A. Oktavina, W. Andrias, S. Pebrina, and I. F. Sukarno, Simfoni Ketahanan Pangan (Menyudahi Stunting, Merangkai Masa Depan Gemilang). Bayfa Cendekia Indonesia, 2024.
I. X. Tim Komisi and P. Negara, “Percepatan Penurunan Stunting Untuk Mewujudkan Sumber Daya Manusia Indonesia Yang Unggul,†2022.
A. G. Putrada, N. Alamsyah, S. F. Pane, and M. Nurkamal Fauzan, “Feature Importance on Text Analysis for a Novel Indonesian Movie Recommender System,†in 2023 11th International Conference on Information and Communication Technology, ICoICT 2023, 2023, pp. 34 – 39. doi: 10.1109/ICoICT58202.2023.10262504.
A. G. Putrada, N. Alamsyah, S. F. Pane, and M. Nurkamal Fauzan, “Feature Importance on Text Analysis for a Novel Indonesian Movie Recommender System,†in 2023 11th International Conference on Information and Communication Technology, ICoICT 2023, 2023, pp. 34 – 39. doi: 10.1109/ICoICT58202.2023.10262504.
A. G. Putrada, N. Alamsyah, M. N. Fauzan, I. D. Oktaviani, and D. Perdana, “GRU for Overcoming Seasonality and Trend in PM2.5 Air Pollution Forecasting,†in 2023 8th International Conference on Informatics and Computing, ICIC 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/ICIC60109.2023.10381963.
M. N. Fauzan and N. M. Saptarini, “Application of the mamdani fuzzy logic multi output method to maintain electrical conductivity in hydroponic media for lettuce (lactuca sativa l.) and bok choy (brassica rapa L.),†Journal of Global Pharma Technology, vol. 12, no. 6, pp. 634 – 640, 2020.
S. Jung, K. H. Lee, M. H. Yoon, and S. E. Kim, “Comparison of adult height prediction using bone age and body composition for growth assessment in Korean children,†Sci Rep, vol. 14, no. 1, 2024, doi: 10.1038/s41598-024-57433-3.
S. Debeko and A. Goshu, “Height Growth Modeling in Ethiopian Children and Adolescents Aged 7–20 Years,†The Mathematical Intelligencer, 2024, doi: 10.1007/s00283-024-10687-z.
C. Tiruneh et al., “Prediction of Body Height Using Hand Length and Hand Breadth in Pharmacy and Nursing Students at Misrake Ghion College, Northeast Ethiopia: An Anthropometry Study,†Acad Forensic Pathol, vol. 14, no. 1, pp. 10–20, 2024, doi: 10.1177/19253621231225259.
H. Yang et al., “Height prediction of individuals with osteogenesis imperfecta by machine learning,†Orphanet J Rare Dis, vol. 19, no. 1, p. 420, 2024, doi: 10.1186/s13023-024-03433-1.
W. Mao, L. Zhang, H. Liu, and Y. Wang, “Study on the relationship between BMI and blood pressure indices in children,†BMC Pediatr, vol. 24, no. 1, p. 123, 2024, doi: 10.1186/s12887-024-04321-7.
H. Shen, H. Zhao, and Y. Jiang, “Machine learning Algorithms for Predicting Stunting among Under-Five Children in Papua New Guinea,†Children, vol. 10, no. 10, 2023, doi: 10.3390/children10101638.
A. Chakraborty, S. Das, and A. Roy, “A comparative study of machine learning algorithms for childhood stunting prediction using NFHS-4 dataset,†Int J Health Sci (Qassim), vol. 17, no. 6, pp. 45–52, 2023, doi: 10.53730/ijhs.v17n6.12345.
E. Nduwayezu, E. Nsabimana, J. B. Bizimana, C. Twahirwa, and P. Tuyisenge, “Spatial machine learning for exploring the variability in low height-for-age from socioeconomic, agroecological, and climate features,†Sci Rep, vol. 14, no. 1, 2024, doi: 10.1038/s41598-024-56712-z.
D. Zeevi, A. Ben Yehuda, D. Nathan, D. Zangen, and L. Kruglyak, “Accurate Prediction of Children’s Target Height from Their Mid-Parental Height,†Children, vol. 11, no. 8, 2024, doi: 10.3390/children11080916.
D. Choukair et al., “Near-Adult Heights and Adult Height Predictions Using Automated and Conventional Greulich–Pyle Bone Age Determinations in Children with Chronic Endocrine Diseases,†Indian J Pediatr, vol. 89, no. 7, pp. 692–698, 2022, doi: 10.1007/s12098-021-04009-8.
İ. M. Erbaş, Z. Ölmez, A. Paketçi, K. Demir, E. Böber, and A. Abacı, “Comparison of the Effectiveness of Adult Height Prediction Methods in Children with Growth Hormone Deficiency,†Endocr Res, vol. 46, no. 3, pp. 140–147, 2021, doi: 10.1080/07435800.2021.1916029.
Mambang, F. D. Marleny, and M. Zulfadhilah, “Prediction of linear model on stunting prevalence with machine learning approach,†Bulletin of Electrical Engineering and Informatics, vol. 12, no. 1, pp. 483–492, Feb. 2023, doi: 10.11591/eei.v12i1.4028.
M. M. Ruiz Brünner, C. Butler, and E. Cuestas, “Development of regression equations for estimating height and weight using body segments in Argentine children,†Nutrition, vol. 57, pp. 122–126, Jan. 2019, doi: 10.1016/j.nut.2018.05.012.
J. Suh et al., “Bone Age Estimation and Prediction of Final Adult Height Using Deep Learning,†Yonsei Med J, vol. 64, no. 11, pp. 679–686, Nov. 2023, doi: 10.3349/ymj.2023.0244.
M. Shmoish et al., “Prediction of Adult Height by Machine learning Technique,†Journal of Clinical Endocrinology and Metabolism, vol. 106, no. 7, pp. E2700–E2710, Jul. 2021, doi: 10.1210/clinem/dgab093.
S. W. Prayoga and S. Pramana, “Hybrid Machine learning to Evaluate the Incidence of Toddler Stunting through Integration of Multi-source Satellite Imagery and Official Statistics in East Nusa Tenggara Province,†2024.
Moh. A. E. Pratama, S. Hendra, H. R. Ngemba, R. Nur, R. Azhar, and R. Laila, “Comparison of Machine learning Algorithms for Predicting Stunting Prevalence in Indonesia,†Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 13, no. 2, pp. 200–209, Jun. 2024, doi: 10.32736/sisfokom.v13i2.2097.
A. M. Sefidian and N. Daneshpour, “Missing value imputation using a novel grey based fuzzy c-means, mutual information based feature selection, and regression model,†Expert Syst Appl, vol. 115, pp. 68–94, Jan. 2019, doi: 10.1016/j.eswa.2018.07.057.
F. Rumapea, E. Fadlyana, M. Dhamayanti, R. Tarigan, R. Rahmayani, and K. Rusmil, “Height Prediction Using the Knee Height Measurement Among Indonesian Children,†Food Nutr Bull, vol. 42, no. 2, pp. 247–258, Jun. 2021, doi: 10.1177/03795721211002067.
T. CELİKTEN, H. Y. DÖNMEZ, T. AKBAS, and O. ALTAY, “Predicting the Height of Individuals with Machine learning Methods by Considering Non-Genetic Factors,†Turkish Journal of Science and Technology, vol. 18, no. 1, pp. 233–241, Mar. 2023, doi: 10.55525/tjst.1248426.
P. H. Nguyen, M. F. Young, L. Q. Khuong, U. Ramakrishnan, R. Martorell, and D. J. Hoffman, “Development of population-specific prediction equations for bioelectrical impedance analyses in Vietnamese children,†British Journal of Nutrition, vol. 124, no. 12, pp. 1345–1352, Dec. 2020, doi: 10.1017/S000711452000241X.
W. F. Blum et al., “A Novel Method for Adult Height Prediction in Children With Idiopathic Short Stature Derived From a German-Dutch Cohort,†J Endocr Soc, vol. 6, no. 7, Jul. 2022, doi: 10.1210/jendso/bvac074.
P. W. H. Cheung, A. Mannem, and J. P. Y. Cheung, “Prediction of Final Body Height for Female Patients With Adolescent Idiopathic Scoliosis,†Global Spine J, vol. 11, no. 6, pp. 833–844, Jul. 2021, doi: 10.1177/2192568220941450.
F. Fitri and M. Almuhayar, “Comparison of Linear Regression and Polynomial Local Regression in Modeling Prevalence of Stunting,†vol. 4, no. 1, p. 2025.
S. J. Mao, G. P. Qian, K. W. Xiao, H. Xu, H. Zhou, and X. J. Guo, “Study on the relationship between body mass index and blood pressure indices in children aged 7–17 during COVID-19,†Front Public Health, vol. 12, 2024, doi: 10.3389/fpubh.2024.1409214.
N. O. Retzepis et al., “Identifying Key Factors for Predicting the Age at Peak Height Velocity in Preadolescent Team Sports Athletes Using Explainable Machine learning,†Sports, vol. 12, no. 11, Nov. 2024, doi: 10.3390/sports12110287.
S. M. J. Rahman et al., “Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach,†PLoS One, vol. 16, no. 6 June 2021, Jun. 2021, doi: 10.1371/journal.pone.0253172.
S. Ndagijimana, I. H. Kabano, E. Masabo, and J. M. Ntaganda, “Prediction of Stunting among Under-5 Children in Rwanda Using Machine learning Techniques,†Journal of Preventive Medicine and Public Health, vol. 56, no. 1, pp. 41–49, Jan. 2023, doi: 10.3961/jpmph.22.388.
H. Yang et al., “Height prediction of individuals with osteogenesis imperfecta by machine learning,†Orphanet J Rare Dis, vol. 19, no. 1, p. 420, Dec. 2024, doi: 10.1186/s13023-024-03433-1.
F. H. Bitew, C. S. Sparks, and S. H. Nyarko, “Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia,†Public Health Nutr, vol. 25, no. 2, pp. 269–280, Feb. 2022, doi: 10.1017/S1368980021004262.
O. N. Chilyabanyama et al., “Performance of Machine learning Classifiers in Classifying Stunting among Under-Five Children in Zambia,†Children, vol. 9, no. 7, Jul. 2022, doi: 10.3390/children9071082.
J. Mkungudza, H. S. Twabi, and S. O. M. Manda, “Development of a diagnostic predictive model for determining child stunting in Malawi: a comparative analysis of variable selection approaches,†BMC Med Res Methodol, vol. 24, no. 1, Dec. 2024, doi: 10.1186/s12874-024-02283-6.
M. M. Khudri, K. K. Rhee, M. S. Hasan, and K. Z. Ahsan, “Predicting nutritional status for women of childbearing age from their economic, health, and demographic features: A supervised machine learning approach,†PLoS One, vol. 18, no. 5 May, May 2023, doi: 10.1371/journal.pone.0277738.
A. Gautama Putrada, I. D. Oktaviani, M. N. Fauzan, and N. Alamsyah, “CNN-LSTM for MFCC-based Speech Recognition on Smart Mirrors for Edge Computing Command,†Journal of Dinda Data Science, Information Technology, and Data Analytics, vol. 4, no. 2, pp. 63–74, 2024, [Online]. Available: http://journal.ittelkom-pwt.ac.id/index.php/dinda
B. Planterose Jiménez, M. Kayser, A. Vidaki, and A. Caliebe, “Adaptive predictor-set linear model: An imputation-free method for linear regression prediction on data sets with missing values,†Biometrical Journal, vol. 66, no. 4, Jun. 2024, doi: 10.1002/bimj.202300090.
A. G. Putrada, N. Alamsyah, M. N. Fauzan, I. D. Oktaviani, and D. Perdana, “GRU for Overcoming Seasonality and Trend in PM2.5 Air Pollution Forecasting,†in 2023 8th International Conference on Informatics and Computing, ICIC 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/ICIC60109.2023.10381963.
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