Cardio Vascular Disease Prediction Based on PCA-ReliefF Hybrid Feature Selection Method with SVM

In the whole world, Cardio Vascular Diseases (CVDs) are the main reason of death. The outcomes of patients are significantly improved by early detection and precise prediction of CVDs. We offer an in-depth process for feature extraction and classification for CVD risk identification in this paper. By combining the strength of Support Vector Machines (SVM) classification with Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA) and PCA with ReliefF feature retrieval methods, this study presents an investigation into feature extraction approaches for CVD classification. On a variety of CVD datasets, tests were run to see how well the PCA-ReliefF feature extraction strategy performed when combined with SVM. In this study, we emphasize the significance of not only accuracy but also recall as a key metric, shedding light on the model’s ability to correctly identify individuals with cardiovascular illnesses. The PCA + ReliefF + SVM model outperforms other algorithms with a consistently higher accuracy ranges from 91.4% to 93.4%, a recall between 82% to 84% and precision ranges from 82% to 84%. The language used for execution is Python.