Known as a pregnancy complication due to high blood pressure and may be accompanied by damage to another organ system, preeclampsia afflicts between 3 and 6 percent of US pregnancies each year. Studies have shown the importance of early detection of preeclampsia to prevent further complications that are detrimental to both mother and infant. In this work, we develop an algorithmic modification of Deep Neural Networks to identify high risk patients in preeclampsia diagnosis using imbalanced datasets in the presence of missing values. We identify the most influential set of clinical features relevant to preeclampsia and train a classifier that can be embedded within a clinical decision support system. Our results provide evidence in favor of increased consideration of patient race/ethnicity in preeclampsia prediction, and for more personalized medicine in general.