IDENTIFYING MOST SUITABLE CLASSIFIER AND RISK FACTORS HIGHLY AFFECT ON LOW BIRTH WEIGHT IN DEVELOPING COUNTRIES: REVIEW
Maqola haqida umumiy ma'lumotlar
Low birth weight (LBW) is a severe public health concern, especially in developing countries, and is often related to child morbidity and mortality. Identifying the essential factors that affect LBW is beneficial and a significant preventive step. It is also a good predictor of the infant's future health concerns. This research is concerned with identifying substantial risk factors for low birth weight and determining the best enihcam -gmnnacal classifier for LBW data. The study reveals that the most critical factors associated with LBW are the mother's age, education of the mother, sex of infants, preceding birth interval, Antenatal care visits (ANC), residence in rural areas, wealth status, and multiple births. Among several studies using machine learning/data mining approaches (i.e., Logistic Regression, Naive Bayes, Neural Networks, Random Forest, Decision Tree, Random Tree, K-Nearest Neighbor, J-48, and support vector machine), the Random Forest algorithm was shown to be the most suitable predictive modeling technique for LBW data.
1. Organization WH. International statistical classification of diseases and related health problems, tenth revision, 2nd ed. World Health Organization; 2004
2. Ikenna K Ndu1, Benedict O Edelu, Samuel N Uwaezuoke, Josephat C Chinawa, Agozie Ubesie, Christian C Ogoke, Kenechukwu K Iloh and Uchenna Ekwochi, Maternal Risk Factors Associated with Low-Birth-Weight Neonates: A Multi-Centre, Cross-Sectional Study in a Developing Country.
3. Rashidul Alam Mahumud, Marufa Sultana, Abdur Razzaque Sarker. distribution and determinants of Low Birth Weight in developing countries.
4. Michele et. al. 2001. Determinants of low birth weight among HIV-infected pregnant women in Tanzania
5. Tessa Wardlaw, Ann Blanc, Geneva; and Elisabeth Åhman, national regional and worwide estimates of low birth weight.
6. Low Birth Weight, children "s hospital of Philadelphia
7. Dasgupta A, Basu R. Determinants of low birth weight in a Block of Hooghly, West Bengal: a multivariate analysis. Int J Biol Med Res 2011;2(4):838-842.
8. World Health Organization. (2019). UNICEF-WHO low birthweight estimates: levels and trends 2000-2015 (No. WHO/NMH/NHD/19.21). World Health Organization
9. Yavar Naddaf, Mojdeh Jalali Heravi and Amit Satsangi., "Predicting Preterm Birth Based on Maternal and Fetal Data".
10. Rabindra Nath Das, Rajkumari Sanatombi Devi, Jinseog Kim, "Mothers' Lifestyle Characteristics Impact on Her Neonates' Low Birth Weight," International Journal of Women's Health and Reproduction Science Volume 2, No. 4, Summer 2014,pp.229– 235.
11. Martina Mueller, Carol L. Wagner, David J. Annibale, Thomas C. Hulsey, Rebecca G. Knapp, and Jonas S. Almeida, "Predicting Extubation Outcome in Preterm Newborns: A Comparison of Neural Networks with Clinical Expertise and Statistical Modeling," Pediatric Research, Volume. 56, No. 1, 2004, International Pediatric Research Foundation, pp. 11-18.
12. Guillermo Marshall, et al., "A New Score for Predicting Neonatal Very Low Birth Weight Mortality Risk in the NEOCOSUR South American Network," Journal of Perinatology, 25, 2005, pp.577-582.
13. Mahajan, and Praveen Kumar, "Predictors of Mortality and Major Morbidities in Extremely Low Birth Weight Neonates," Indian Pediatrics, Volume 50, December 15, 2013,pp.1119-1123.
14. Ghaderi ghahfarokhi, S., Sadeghifar, J., & Mozafari, M. (2018). A model to predict low birth Weight infants and affecting factors using data mining techniques. Journal of Basic Research in Medical Sciences, 5(3), 1-8.
15. Gupta, R. D., Swasey, K., Burrowes, V., Hashan, M. R., & Al Kibria, G. M. (2019). Factors associated with low birth weight in Afghanistan: a cross-sectional analysis of the demographic and Health survey 2015. BMJ Open, 9(5), e025715.
16. Hange, U., Selvaraj, R., Galani, M., & Letsholo, K. (2017, May). A Data-Mining Model for Predicting Low Birth Weight with a High AUC. In International Conference on Computer and Information Science (pp. 109-121). Springer, Cham.
17. Eliyati, N., Faruk, A., Kresnawati, E. S., & Arifieni, I. (2019, July). Support vector machines For the classification of low birth weight in Indonesia. In Journal of Physics: Conference Series (Vol. 1282, No. 1, p. 012010). IOP Publishing.
18. Yadav, H., Lee, N.: Maternal factors in predicting low birth weight babies. Med. J. Malays. 68 (1), 44–47 (2013)
19. Marshall, G., Tapia, J.L., Ivonne, D., Grandi, C., Barros, C., Alegria, A., Standen, J., Panizza, R., Bancalari, A., Lacarruba, J., Fabres, J.: A new score for predicting neonatal very low birth Weight mortality risk in the NEOCOSUR South American network. J. Perinatol. 25, 577–582 (2005)
20. Senthilkumar, D., Paulraj, S.: Prediction of low-birth-weight infants and its risk factors using Data mining techniques. In: Proceedings of the 2015 International Conference on Industrial Engineering and Operations Management, pp. 186–194 (2015)
21. Desalegn, B.: Predicting Low Birth Weight Using Data Mining Techniques on Ethiopia Demographic and Health Survey Data Sets. Addis Ababa University (2011)
22. Ashdown-Lambert, J. R. (2005). A review of low birth weight: predictors, precursors, and Morbidity outcomes. The journal of the Royal Society for the Promotion of Health, 125(2), 7683.
23. A. Zahirzada and K. Lavangnananda, "Implementing Predictive Model for Low Birth Weight in Afghanistan," 2021 13th International Conference on Knowledge and Smart Technology (KST), 2021, pp. 67-72, DOI: 10.1109/KST51265.2021.9415792.
Zahirzada, A. ., Akbar Shahpoor, M. ., & Niazai, A. . (2022). IDENTIFYING MOST SUITABLE CLASSIFIER AND RISK FACTORS HIGHLY AFFECT ON LOW BIRTH WEIGHT IN DEVELOPING COUNTRIES: REVIEW. Academic Research in Educational Sciences, 3(12), 157–162. https://doi.org/
Zahirzada, Abdullah, et al. “IDENTIFYING MOST SUITABLE CLASSIFIER AND RISK FACTORS HIGHLY AFFECT ON LOW BIRTH WEIGHT IN DEVELOPING COUNTRIES: REVIEW.” Academic Research in Educational Sciences, vol. 12, no. 3, 2022, pp. 157–162, https://doi.org/.
Zahirzada, ., Akbar Shahpoor, ., and Niazai, . 2022. IDENTIFYING MOST SUITABLE CLASSIFIER AND RISK FACTORS HIGHLY AFFECT ON LOW BIRTH WEIGHT IN DEVELOPING COUNTRIES: REVIEW. Academic Research in Educational Sciences. 12(3), pp.157–162.