Predicting risk of low birth weight offspring from maternal features and blood polycyclic aromatic hydrocarbon concentration.

• Nine polycyclic aromatic hydrocarbons and cotinine were found in the cord blood, confirming its transfer to the fetus. • Machine learning approach used in the study provides an insight to distinguish between low and normal birth weight babies. • Proposed pLBW webserver predicts the state of the baby with a maximum accuracy of 82.42% using SVM algorithm. Prenatal exposure to organic pollutants increases the risk of low birth weight (LBW) offspring. Women involved in the plucking of tea leaves can be exposed to polycyclic aromatic hydrocarbons (PAHs) during pregnancy through inhalation and diet. Therefore, the aim of the study was to investigate the association of maternal socio-demographic features and blood PAH concentration with LBW; also to develop a model for predicting LBW risk. The study was performed by recruiting 55 women who delivered LBW and 120 women with NBW (normal birth weight) babies from Assam Medical College. The placental tissue, maternal and cord blood samples were collected. A total of sixteen PAHs and cotinine were analysed by HPLC and GC-MS. Association of PAH concentration with weight was determined using correlation and multiple logistic regression analyses. Predictive model was developed using SVM light and Weka software. Maternal features such as age, education, food habits, occupation, etc. were found to be associated with LBW deliveries (p-value<0.05). Overall, 9 PAHs and cotinine were detected in the samples. A multiple logistic regression depicted an increased likelihood of LBW by exposure to PAHs (pyrene, di-benzo (a,h) anthracene, fluorene and fluoranthene) and cotinine. Models based on the features and PAHs/ cotinine predicted LBW offspring with 84.35% sensitivity and 74% specificity. LBW prediction models are available at http://dev.icmr.org.in/plbw/ webserver. With machine learning gaining more importance in medical science; our webserver could be instrumental for researchers and clinicians to predict the state of the fetus. [ABSTRACT FROM AUTHOR]