A model to predict low birth weight infants and affecting factors using data mining techniques

Introduction: Birth weight is a reliable indication of intrauterine growth and determines the child's future physical and intellectual development. The purpose of this study was to use data mining technique in identifying accurate predictors of (low birth weight) LBW. Materials and methods: This study used secondary data from 450 medical records of newborns in the educational Hospitals affiliated to Ilam University of Medical Sciences. The birth records were reviewed from April 2015 to April 2016. The checklist used to collect data comprised of two parts: demographic and effective factors (13 factors of medical and neonatal, 4 factors of mother's lifestyle and 8 about mother factors). Data were analyzed by SPSS version 21 and WEKA software. Results: Our findings showed that mean weight of infants was 2289 ± 864 gr. The mean gestational age was 35.2 ± 4.63 weeks. 14.9% of mothers suffer from placenta previa and 14.4% suffer from preeclampsia. The results of ANOVA showed that neonatal weight was significantly higher among mothers with weight range of 84-110 Kg. The random forest algorithm showed that gestational age less than 36 weeks is main predictor and number of fetuses, preeclampsia, and premature rupture of membrane, placenta previa, the number of pregnancies and the degree of mother education were other predictors of low birth weight. Conclusion: This study confirmed that low birth weight is a multifactorial condition requiring a systematic and accurate program to reduce LBW. Individual and group education through mass media, repeated monitoring of pregnant mothers, activation of the referral system and pursuit of a family health care technician may reduce prevalence of LBW.