Improved Classification using Extended Hybrid Feature Selection Approach with Dimensionality Reduction

Heart Disease has become the major cause of death around the world. The World Health Organization has recorded around 17 million deaths caused by cardiovascular heart disease (CVD). This is popular among the low and middle-income population, where resources and benefits of healthcare programs are lacking and people are not able to pay for the expensive procedures. Hence, having an efficient and effective solution in detecting heart disease occurrence is crucial. In this research, a novel approach of hybrid feature selection with injection of feature dimensionality was studied. Where the proposed method combines the strength of different feature selection techniques and enhanced by dimensionality reduction techniques. This research has used various sources of heart disease dataset from Cleveland, Framingham and Z-Alizadash to study the potential of generalization of the proposed method. In this research, KNN and SVM were applied to tested the proposed feature selection engine. The performance of the feature subsets will be evaluated using various machine learning models. The model performance will be compared and studied using accuracy.