Using inductive machine learning, expert systems and case based reasoning to predict preterm delivery in pregnant women

Abstract

A previously constructed prototype expert system was extended to include case-based reasoning and learning, in order to improve the system's predictive accuracy in assessing preterm delivery risk. The initial expert system was developed by using an inductive machine learning technique on 9,445 data records of pregnant women, providing production rules to predict preterm delivery. Its predictive accuracy was tested on a separate set of 9,445 data records. Next, the capability to reason from both production rules and input test cases was added to the system, in addition to the capability to internally modify its confidence in each piece of knowledge (rule or case) and the relative importance of patient attributes which appear to be predictive of preterm delivery. The system was structured such that the accuracy of either type of reasoning could be measured individually to determine how rule-based and case-based reasoning perform alone, and to determine how they perform together. Results show that the predictive accuracy of the system was improved, with different trends emerging, dependent on the bias of the learning data, with the hybrid system providing the best predictive accuracy.