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Preterm birth (PTB) is an unfavorable and unfortunately frequent result of a pregnancy in the countries. Its prevalence is reported 11%-12.9% in the world (1). The concept of preterm refers to an infant born earlier than 37 weeks of pregnancy. The main cause of PTB is unknown. However, risk factors such as infections, smoking, hypertension, low body mass index, low educational level and drug consumption of mothers and working during pregnancy are reported as effective factors in PTB (2). PTB leads to more than 80% mortality of infants in the third world countries (3). The other important problem in infants is low birth weight (LBW). An infant with birth weight less than 2500 g is called LBW. The mortality of LBW infants is 40 times more than normal weight ones (4). The primary causes of LBW are PTB, intrauterine growth restriction or their combination (5). Some other significant factors in this event include race, low socio-economic status, low height, malnutrition and low weight of the mother accompanied with the history of LBW, uterine or cervical anomalies, first delivery, chronic disease, smoking, twin or multi births, anemia, etc (4). To prevent these events, determining the effective factors is important through up to date etiological studies. In addition, since these factors are affected by social and economic conditions and geographical living area, identifying local factors is necessary. Various researches have previously been conducted on this topic in Iran (4,6-9) and all over the world (1-3,1017). Most of them have utilized logistic regression to find the significant factors. The other modeling methods were rarely applied (10, 15). The attempt in the present study was to use the high modeling performance of artificial neural networks (ANNs) method in modeling complex relations to investigate the effective factors in PTB and LBW in Shiraz, southern Iran.

A Comparison of the Effective Factors of Preterm Birth Versus Low Birth Weight in Southern Iran Using Artificial Neural Network