Data Mining Approach in Preterm Birth Prediction

Abstract

Data mining refers to the process of discovering patterns in data, typically with the aid of powerful algorithms to automate part of the search. These methods come from the disciplines such as statistics, machine learning, pattern recognition, neural networks and database. In particular this paper reveals out how the problem of preterm birth prediction is approached by a data mining analyst with a background in machine learning. In the health field, data mining applications have been growing considerably as it can be used to directly derive patterns, which are relevant to forecast different risk groups among the patients. Data mining technique such as clustering has not been used to predict preterm birth. Hence this paper made an attempt to identify patterns from the database of the preterm birth patients using clustering.