Unsupervised Feature Selection Approaches for Medical Dataset Using Soft Computing Techniques

An electrocardiogram (ECG) measures the heart's electrical function and has been widely used to detect heart disorders because of their simplicity and non-obtrusive nature. Nowadays, in medical decision support systems, soft computing methods are commonly used. Medical diagnosis helps to acquire various characteristics reflecting the disease's various variations. It is likely to have important, irrelevant, and redundant features to reflect disease, with the aid of various diagnostic procedures. It is a non-trivial job to define a good feature subset for efficient classification. The objective of this research is to assess three different soft computing-based unsupervised feature reduction approaches namely Soft Set-Based Unsupervised Feature Reduction, Unsupervised Quick Reduction, and Unsupervised Relative Reduction, to evaluate the best subset of features with enhanced diagnostic classification accuracy for cardiovascular disease. The output of feature selection algorithms is evaluated with the heart disease dataset from the UCI repository. The experimental findings indicate that the Soft Set-based Unsupervised Feature Redact algorithm achieved good classification accuracy compared to other feature reduction approaches.