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| Abstract : Universiti Pendidikan Sultan Idris |
| Overweight and underweight are common issues among children in Malaysia and thus, Malaysian government has launched several campaigns to combat these problems. Previous research show that BMI at certain age could be predicted earlier so that early intervention can be initiate. Most of the study are very specific to some locality and certain ethnic, make it hard to generalize to the whole Malaysian. This research aims to investigate significant positive predictors of changes in Body Mass Index (BMI) of young children aged from 12 months to 48 months and to develop a prediction model for their BMI status (normal, overweight, obese, risk of overweight, underweight, and severe underweight). Data consisting of 558 records with 16 attributes involving 18,226 children, who had registered at the National Child Data Centre (NCDC) were analyzed and predicted using the decision tree algorithms of Rapid miner. The BMI of 48 months-old children can be reliably predicted by their BMI statuses at the ages of 21, 24, 27, 36, 42, and 45 months, parents? incomes, and gender while the BMI at the age of 20 months and below are the negative predictors. The prediction model developed in this study may help practitioners to monitor such children?s BMI, especially in detecting the signs of severe overweight, obesity, and underweight among such children. Such a BMI prediction model can be used to identify young children who are at risk of being overweight, obese, and underweight such that remedial interventions can be implemented promptly. Copyright 2021 by The Pacific |
| References |
(0000). Consumer Price Index Malaysia April 2020, Retrieved from www.scopus.com (2012). Childhood Obesity World Health Organization 2012, Retrieved from www.scopus.com (2014). Healty Life Style 2014, Retrieved from www.scopus.com Ameer, M., Abeer, A., Abrar, A., Rahaf, K., Ahmed, A., Hanadi, A., . . . Abdulmoein, A. (2018). Parental socioeconomic status and occupation in relation to childhood obesity. International Journal of Medicine in Developing Countries, 4(3), 576-585. Retrieved from www.scopus.com Amin, M. S., Chiam, Y. K., & Varathan, K. D. (2019). Identification of significant features and data mining techniques in predicting heart disease. Telematics and Informatics, 36, 82-93. doi:10.1016/j.tele.2018.11.007 Apouey, B. H. (2016). Child physical development in the UK: The imprint of time and socioeconomic status. Public Health, 141, 255-263. doi:10.1016/j.puhe.2016.09.004 Aslan, A. A., & Sulaiman, N. (2020). Overweight and obesity among children: A relationship between maternal beliefs and feeding practices with children's body mass index-for-age in bandar and jugra kuala langat selangor. Malaysian Journal of Medicine and Health Sciences, 16(6), 11-18. Retrieved from www.scopus.com Bentham, J., Di Cesare, M., Bilano, V., Bixby, H., Zhou, B., Stevens, G. A., . . . NCD Risk Factor Collaboration (NCD-RisC). (2017). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: A pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. The Lancet, 390(10113), 2627-2642. doi:10.1016/S0140-6736(17)32129-3 Bhattacharya, M., Ehrenthal, D., & Shatkay, H. (2014). Identifying growth-patterns in children by applying cluster analysis to electronic medical records. Paper presented at the Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014, 348-351. doi:10.1109/BIBM.2014.6999183 Retrieved from www.scopus.com Boseley, S. (2019). 12 Signs to Predict if Your Baby Will be Overweight by Age 10, Retrieved from www.scopus.com De Kat, A. C., Hirst, J., Woodward, M., Kennedy, S., & Peters, S. A. (2019). Prediction models for preeclampsia: A systematic review. Pregnancy Hypertension, 16, 48-66. doi:10.1016/j.preghy.2019.03.005 D'heygere, T., Goethals, P. L. M., & De Pauw, N. (2003). Use of genetic algorithms to select input variables in decision tree models for the prediction of benthic macroinvertebrates. Ecological Modelling, 160(3), 291-300. doi:10.1016/S0304-3800(02)00260-0 Dreimüller, N., Lieb, K., Tadić, A., Engelmann, J., Wollschläger, D., & Wagner, S. (2019). Body mass index (BMI) in major depressive disorder and its effects on depressive symptomatology and antidepressant response. Journal of Affective Disorders, 256, 524-531. doi:10.1016/j.jad.2019.06.067 Fitzsimons, E., & Pongiglione, B. (2019). The impact of maternal employment on children's weight: Evidence from the UK. SSM - Population Health, 7 doi:10.1016/j.ssmph.2018.100333 Furlong, K. R., Anderson, L. N., Kang, H., Lebovic, G., Parkin, P. C., Maguire, J. L., . . . TARGet Kids! Collaboration. (2016). BMI-for-age and weight-for-length in children 0 to 2 years. Pediatrics, 138(1) doi:10.1542/peds.2015-3809 Gibert, K., Izquierdo, J., Sànchez-Marrè, M., Hamilton, S. H., Rodríguez-Roda, I., & Holmes, G. (2018). Which method to use? an assessment of data mining methods in environmental data science. Environmental Modelling and Software, 110, 3-27. doi:10.1016/j.envsoft.2018.09.021 Hartley, E. M., Hoch, M. C., & Boling, M. C. (2018). Y-balance test performance and BMI are associated with ankle sprain injury in collegiate male athletes. Journal of Science and Medicine in Sport, 21(7), 676-680. doi:10.1016/j.jsams.2017.10.014 Jabakhanji, S. B., Boland, F., Ward, M., & Biesma, R. (2018). Body mass index changes in early childhood. Journal of Pediatrics, 202, 106-114. doi:10.1016/j.jpeds.2018.06.049 Jackson, J. (2002). Data mining: A conceptual overview. Communications of the Association for Information Systems, 8(19), 267-296. Retrieved from www.scopus.com Kavitha, R., & Kannan, E. (2016). An efficient framework for heart disease classification using feature extraction and feature selection technique in data mining. Paper presented at the 1st International Conference on Emerging Trends in Engineering, Technology and Science, ICETETS 2016 - Proceedings, doi:10.1109/ICETETS.2016.7603000 Retrieved from www.scopus.com Law, L. S., Gan, W. Y., & Mohd Taib, M. N. (2020). Sociodemographic and psychological factors as predictors of body mass index-for-age (BAZ) among adolescents in sibu, malaysia. Malaysian Journal of Medicine and Health Sciences, 16(6), 56-62. Retrieved from www.scopus.com Lum, M. (2018). Malaysia is Asia’s Fattest Country, Retrieved from www.scopus.com Nazarova, E., & Kuzmichev, Y. (2016). The height-, weight-and BMI-for-age of preschool children from nizhny novgorod city, russia, relative to the international growth references. BMC Public Health, 16(1) doi:10.1186/s12889-016-2946-8 Noh, J. -., Kim, Y. -., Oh, I. -., & Kwon, Y. D. (2014). Influences of socioeconomic factors on childhood and adolescent overweight by gender in korea: Cross-sectional analysis of nationally representative sample. BMC Public Health, 14(1) doi:10.1186/1471-2458-14-324 Oberle, C. D., Samaghabadi, R. O., & Hughes, E. M. (2017). Orthorexia nervosa: Assessment and correlates with gender, BMI, and personality. Appetite, 108, 303-310. doi:10.1016/j.appet.2016.10.021 Perveen, S., Shahbaz, M., Guergachi, A., & Keshavjee, K. (2016). Performance analysis of data mining classification techniques to predict diabetes. Paper presented at the Procedia Computer Science, , 82 115-121. doi:10.1016/j.procs.2016.04.016 Retrieved from www.scopus.com Raymond Leprince, J., Sariman, S., & Basir Mohammed, R. B. (2020). Parental child feeding practices and growth status of orang asli children in negeri sembilan, malaysia. British Food Journal, 122(10), 3239-3248. doi:10.1108/BFJ-01-2020-0053 Rosas, L. G., Guendelman, S., Harley, K., Fernald, L. C. H., Neufeld, L., Mejia, F., & Eskenazi, B. (2011). Factors associated with overweight and obesity among children of mexican descent: Results of a binational study. Journal of Immigrant and Minority Health, 13(1), 169-180. doi:10.1007/s10903-010-9332-x Shuhaimi, F., & Muniandy, N. D. (2012). The association of maternal employment status on nutritional status among children in selected kindergartens in selangor, malaysia. Asian Journal of Clinical Nutrition, 4(2), 53-66. doi:10.3923/ajcn.2012.53.66 Stuart, B., & Panico, L. (2016). Early-childhood BMI trajectories: Evidence from a prospective, nationally representative british cohort study. Nutrition and Diabetes, 6 doi:10.1038/nutd.2016.6 Swani, L., & Tyagi, P. (2017). Predictive modelling analytics through data mining. International Research Journal of Engineering and Technology (IRJET), 4(9) Retrieved from www.scopus.com Tan, L. (2015). Code comment analysis for improving software quality. The art and science of analyzing software data (pp. 493-517) doi:10.1016/B978-0-12-411519-4.00017-3 Retrieved from www.scopus.com te Riele, R. J. L. M., Dronkers, E. A. C., van den Brink, M. H., De Herdt, M. J., Sewnaik, A., Hardillo, J. A., & Baatenburg de Jong, R. J. (2018). Influence of anemia and BMI on prognosis of laryngeal squamous cell carcinoma: Development of an updated prognostic model. Oral Oncology, 78, 25-30. doi:10.1016/j.oraloncology.2018.01.001 Teh, S. C., Asma’, A., Hamid, J. J. M., & Yusof, H. M. (2020). Assessment of food security, anthropometric and cognitive function among orang asli children in pahang, malaysia. IIUM Medical Journal Malaysia, 19(3), 81-91. doi:10.31436/IMJM.V19I3.1669 Wafa, S. W., & Ghazalli, R. (2020). Association between the school environment and children’s body mass index in terengganu: A cross sectional study. PLoS ONE, 15(4) doi:10.1371/journal.pone.0232000 Woon, F. C., Chin, Y. S., & Mohd Nasir, M. T. (2015). Association between behavioural factors and BMI-for-age among early adolescents in hulu langat district, selangor, malaysia. Obesity Research and Clinical Practice, 9(4), 346-356. doi:10.1016/j.orcp.2014.10.218 Wu, H., Yang, S., Huang, Z., He, J., & Wang, X. (2018). Type 2 diabetes mellitus prediction model based on data mining. Informatics in Medicine Unlocked, 10, 100-107. doi:10.1016/j.imu.2017.12.006 Yeh, D. -., Cheng, C. -., & Chen, Y. -. (2011). A predictive model for cerebrovascular disease using data mining. Expert Systems with Applications, 38(7), 8970-8977. doi:10.1016/j.eswa.2011.01.114 Zhang, J., Xu, L., Li, J., Sun, L., Qin, W., Ding, G., . . . Zhou, C. (2019). Gender differences in the association between body mass index and health-related quality of life among adults:A cross-sectional study in shandong, china. BMC Public Health, 19(1) doi:10.1186/s12889-019-7351-7 Ziauddeen, N., Roderick, P. J., Macklon, N. S., & Alwan, N. A. (2018). Predicting childhood overweight and obesity using maternal and early life risk factors: A systematic review. Obesity Reviews, 19(3), 302-312. doi:10.1111/obr.12640 |
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