Extraction and Feature Selection for Precise Cardiovascular Disease Classification

Heart disease causes significant mortality rates worldwide and has become a health threat for many people. Thus, proper monitoring and early detection of cardiac disease can decrease the significant problem when attempting to forecast heart disease. Accurate prediction of heart disease from clinical data is a significant challenge. This study aims to develop an autonomous system capable of diagnosing heart disease by employing feature extraction and selection techniques. The majority of the time, decision support systems are employed for automated disease diagnosis in humans. The choice of the most relevant characteristics has a major impact on these systems' performance. This research aims to develop a feature extraction methodology to identify relevant patterns within heart disorder data. These extracted features will subsequently be employed to classify various heart conditions. This analysis demonstrates a methodology that may be used to find a lower dimensional collection of features from test data and utilize those features to diagnose heart disease. The presented methodology utilizes Probabilistic Principal Component Analysis (PPCA) that capture high impact characteristics in a new projection. With the use of PPCA, projection vectors with the highest covariance contributions are extracted and used to minimize feature dimension. The method's performance was evaluated using standard metrics: accuracy, specificity, and precision. PPCA demonstrated superior performance in classifying heart disease compared to other methods, achieving an accuracy, specificity, and precision