This thesis describes a new methodology used in conjunction with artificial intelligence tools to create multiple models for prediction of preterm birth in obstetrical environments. The data mining approach integrates: Decision Trees (DTs), Artificial Neural Networks (ANNs) - specifically a Feed Forward Back Propagation ANN, and Case Based Reasoning System (CBRS).

This work also introduces a 5by2 cross validation method, assesses two methods of attribute selection, and considers data prevalence (15% and 8.1%) in training and testing networks. Two databases were assessed from two countries: BORN (Canada) and PRAMS (USA).

Best BORN results used selection method 2, had sensitivities of 50.53%, 53.96%, specificities of 91.61%, 95.40%, and area under curves (AUC) of 0.7721, 0.7970 for Parous and Nulliparous cases respectively. Best PRAMS results used selection method 1, had sensitivities of 68.15%, 40.35%, specificities of 64.71%, 94.57%, and area under curves (AUC) of 0.8452, 0.7064 for Parous and Nulliparous cases respectively.