Comparative Analysis of Gestational Diabetes using Data Mining Techniques

Abstract

Data mining is process of extracting hidden knowledge from large volumes of raw data. Data mining is used to discover knowledge out of data and presenting it in a form that is easily understand to humans Disease Prediction plays an important role in data mining. Medical data mining has great potential for exploring the hidden patterns in the data sets of the medical domain. Data Mining is used intensively in the field of medicine to predict Gestational diabetics which is affected the pregnant women. Gestational diabetes mellitus (GDM) is defined as any degree of glucose intolerance with onset or first recognition during pregnancy. This paper analyzes the Gestational diabetics predictions using different classification algorithms. Medicinal data mining has high potential for exploring the unknown patterns in the data sets of medical domain. These patterns can be used for medical analysis in raw medical data using decision table, Multi-layer perceptron and Naives Bayes algorithm and number of experiment has been conducted in WEKA tool to compare the performance of predictive data mining technique on the same dataset and the outcome reveals that Naives Bayes algorithm outperforms than other predictive methods such as Decision table, Multi-layer perceptron.