A Study on Performance Improvement of Heart Disease Prediction by Attribute Selection Methods

Abstract

Heart pumps blood for all tissues of the body. The deteriorate of this organ causes a severe illness, disability and death sincecardiovascular diseases involve the diseases that related to heart and circulation system. Determination of the significance offactors affecting this disease is of great importance for early prevention and treatment of this disease. In this study, firstly, thebest attributes set for Single Proton Emission Computed Tomography (SPECT) and Statlog Heart Disease (STATLOG) datasetswere detected by using feature selection methods named RFECV (Recursive Feature Elimination with cross-validation) and SS(Stability Selection). Secondly, GBM (Gradient Boosted Machines), NB (Naive Bayes) and RF (Random Forest) algorithmswere implemented with original datasets and with datasets having selected attributes by RFECV and SS methods and theirperformances were compared for each dataset. The experimental results showed that maximum performance increases wereobtained on SPECT dataset by 14.81% when GBM algorithm was applied using attributes provided by RFECV method and onSTATLOG dataset by 6.18% when GBM algorithm was applied using attributes provided by RFECV method. On the other hand,best accuracies were obtained by NB algorithm when applied using attributes of SPECT dataset provided by RFECV methodand using attributes of STATLOG dataset provided by SS method. The results showed that medical decision support systemswhich can make more accurate predictions could be developed using enhanced machine learning methods by RFECV and SSmethods and this can be helpful in selecting the treatment method for the experts in the field.