The Comparison of ReliefF and C.45 for Feature Selection on Heart Disease Classification Using Backpropagation

Abstract

One of the datasets used to classify heart disease is UCI dataset. unfortunately, the dataset contains missing data. Backpropagation is an easy and fast method, but it is very dependent on input data so if there is missing data, it can reduce the performance of the backpropagation. One of the techniques used to handle missing data is feature selection. This study compares ReliefF and C4.5 algorithm in feature selection. The purpose of the study is to find way in overcoming missing data by feature selection to improve backpropagation performance in the heart disease classification. The results of these algorithms are applied to the classification by Backpropagation. The results will be measured based on accuracy, precision, and recall. The performance results of the ReliefF and Backpropagation are above 82%. The performance results of of C4.5 and backpropagation are 80.54% on average for accuracy, recall and precision. Based on the results it can be concluded the ReliefF gives better performance on backpropagation than C4.5. ReliefF is also able to handle missing data by performing feature selection to improve the performance of the backpropagation method for heart disease classification compared to C4.5. Although the C4.5 algorithm is able to provide increased performance on backpropagation, C4.5 is not appropriate to be used as a feature selection method for handling missing data.

Keywords

• data mining
• machine learning
• mathematic
• Backpropagation
• C4.5
• Feature Selection
• Missing Data
• ReliefF