Intelligent Syncope Disease Prediction Framework using DM-Ensemble Techniques

Abstract

Data mining can be used in various fields’ i.e. mobile computing, web mining, expert predictions, crime analysis, engineering, management and medicine. In medical field, data mining techniques can be used by the researchers for the diagnosis and prediction of various diseases. A framework is proposed to predict Syncope Disease using Ensemble technique that contains Naïve Bayes, Gini Index and Support Vector Machine classifiers. Patient’s data set for this research work is obtained from Armed Forces Institute of Cardiology (AFIC & NIHD) in Pakistan. Thirty one attributes have been used to predict Syncope using Ensemble techniques but each technique uses its own way to predict Syncope based on specific rules. In the end results are compared and accuracy is measured on majority voting from applied data mining ensemble techniques. Results prove that proposed research framework is accurate and can be used for future development.