Open AccessClassification and Feature Selection Approaches by Machine Learning Techniques: Heart Disease Prediction
Author(s) -
N. Satish Chandra Reddy,
Song Shue Nee,
Lim Zhi Min,
Chew Xin Ying
Publication year - 2019
Publication title -
international journal of innovative computing
Language(s) - English
Resource type - Journals
ISSN - 2180-4370
DOI - 10.11113/ijic.v9n1.210
Subject(s) - feature selection , random forest , machine learning , computer science , heart disease , feature (linguistics) , artificial intelligence , selection (genetic algorithm) , predictive modelling , data mining , pattern recognition (psychology) , medicine , cardiology , linguistics , philosophy
The heart disease has been one of the major causes of death worldwide. The heart disease diagnosis has been expensive nowadays, thus it is necessary to predict the risk of getting heart disease with selected features. The feature selection methods could be used as valuable techniques to reduce the cost of diagnosis by selecting the important attributes. The objectives of this study are to predict the classification model, and to know which selected features play a key role in the prediction of heart disease by using Cleveland and statlog project heart datasets. The accuracy of random forest algorithm both in classification and feature selection model has been observed to be 90–95% based on three different percentage splits. The 8 and 6 selected features seem to be the minimum feature requirements to build a better performance model. Whereby, further dropping of the 8 or 6 selected features may not lead to better performance for the prediction model.