Early diagnosis of coronary artery disease using UCI data set

Abstract

The focus of this project is based on both processing potential clinical features and implementing the classification architecture for detection of cardiac abnormality. The milestone of this first year involves analysis and investigation of different feature selection and transformation methods and theoretical modeling of single and hybrid systems by optimizing associated systematic parameters for better precision and recall. The importance of this paper is due to its clear objectives where an optimized and advanced system is designed and implemented for the cardiac disease utilizing computer aided diagnosis techniques for data and signal processing. The methodology is clear and trait forward using the hybrid approach of data mining techniques integrated to deliver enhanced performance on desired data set. In this paper comparative classification approaches are integrated to enhance system detection rate and decrease false alarms. The study focuses on feature pre-processing to select suitable feature subsets for classification algorithms like clustering (unsupervised learning) and SVM (supervised learning) which helps in generalizing the diagnosis system to detect unseen abnormality. For this study, will first apply statistical measures such as scoring ranking for clinical datasets consisting the electrocardiogram (ECG) features to reduce its dimension by eliminating irrelevant features. In the second part, will apply parametric tuned classification algorithms for selected feature subsets. The third part is to quantify the severity of CAD. At the last performance of the proposed system will be compared with other applied classification techniques in terms of accuracy, sensitivity and specificity.