  In this paper, we propose Ensemble Learning models to identify factors
contributing to preterm birth. Our work leverages a rich dataset collected by a
NIEHS P42 Center that is trying to identify the dominant factors responsible
for the high rate of premature births in northern Puerto Rico. We investigate
analytical models addressing two major challenges present in the dataset: 1)
the significant amount of incomplete data in the dataset, and 2) class
imbalance in the dataset. First, we leverage and compare two types of missing
data imputation methods: 1) mean-based and 2) similarity-based, increasing the
completeness of this dataset. Second, we propose a feature selection and
evaluation model based on using undersampling with Ensemble Learning to address
class imbalance present in the dataset. We leverage and compare multiple
Ensemble Feature selection methods, including Complete Linear Aggregation
(CLA), Weighted Mean Aggregation (WMA), Feature Occurrence Frequency (OFA), and
Classification Accuracy Based Aggregation (CAA). To further address missing
data present in each feature, we propose two novel methods: 1) Missing Data
Rate and Accuracy Based Aggregation (MAA), and 2) Entropy and Accuracy Based
Aggregation (EAA). Both proposed models balance the degree of data variance
introduced by the missing data handling during the feature selection process
while maintaining model performance. Our results show a 42\% improvement in
sensitivity versus fallout over previous state-of-the-art methods.
