An improved ensemble learning approach for the prediction of cardiovascular disease using majority voting prediction

Coronary heart disease (CHD) is one of the most common heart disease types in the world. It becomes a frequent cause of mortality due to a lack of proper medical diagnosis, technology, and a healthy lifestyle. The machine learns patterns from an existing dataset and applies different rules to predict the outcome. Classification is a powerful machine learning technique for prediction. In this work, we propose a new ensemble classification model by combining multiple classifiers for improving the accuracy of weak algorithms. An ensemble classifier was applied by using a majority vote-based technique for cardiovascular disease prediction and classification. A three-dimensionality approach is applied to Cleveland dataset from the UCI repository. The average accuracy of each method is calculated as PCA (0.8636), K-PCA (0.8630), and LDA (0.90). Compared to PCA and K-PCA, higher accuracy is achieved by LDA. LDA is used as the best dimensionality reduction technique.