Suggesting Mode of Delivery by Using Iterative Dichotomiser3 (ID3) Algorithm

Abstract

Data mining is a process that has a variety of data analysis tools to discover patterns and relationships in data that may be used to make valid predictions. Classification is the process of finding a set of models that describe and distinguish data classes or concepts , for the purpose of being able to use the model to predict the class of objects whose class label is unknown. Classification of complex measurements is used in many application domain. This system intended to implement a suggesting system for OG (Obetetrics Gyanaecology) knowledge in predicting mode of delivery (method of labour process) by using ID3 classification method. Patient’s 4 CTG outline information , patient’s age , patient’s gestation week , condition of AF (Amniotic Fluid) guess , condition of fetal distress guess are used for predicting mode of delivery. Depending on these 8 attributes values , the system can generate two categories of mode of delivery (namely :Normal Vaginal Delivery [NVD] and Lower Segment Caesarean Section [LSCS]) for new born baby. This system use hold-out accuracy method to approve the system accuracy.