Building a Classification Model to Identify the Effect of Apgar Score Factor on Cesarean Operation

Abstract

In today’s world, gigantic amount of data is available in medical domain, science, industry, business and many other areas. The data can provide valuable information which can be used by management for making important decisions. The research is focused on comparison of various classification algorithms using WEKA tool to obtain the highest accuracy. The aim of research building a classification model to identify the effect Apgar factor on cesarean operation. Applied five data mining classification techniques were used in the research, it included j48, IBK, SMO, NB, and MLP algorithms. The results of performance classification algorithms nearly same, but the highest accuracy 96.8 % obtained IBK algorithm using 10 cross validation. Used statistical analysis for some attributes for interesting information, such as an approximately53.74% women that gave birth natural operation and 46.26% women took cesarean operation, and another used statistical analysis to the Apgar score based on data from the mother, newborn and medical interventions. it values normal obtained approximately 80%,low of approximately12.9%, very low of approximately 7.1%. Data mining techniques are useful for effect Apgar factor on cesarean operation to obtained optimal Apgar score.