Ensemble-based Heart Disease Diagnosis (EHDD) using Feature Selection and PCA Extraction Methods

Heart disease is a growing health crisis in India, with mortality rates on the rise alongside the population. Numerous studies have been undertaken to understand, predict, and prevent this critical illness. The dimensionality of the dataset, on the other hand, reduces the prediction's accuracy.

We propose an Ensemble-based Heart Disease Diagnosis (EHDD) model in which the dimension is lowered through filter-based feature selection. The experimental is conducted using the UCI Cleveland dataset with cardiac disease. The precision is achieved through three key steps. The scatter matrix is utilized to divide the distinct class points in the first phase, and the highest eigenvalue and eigenvectors are picked for the new decreased dimension of the dataset. The feature extraction is carried out in the second stage utilizing a statistical approach based on mean, covariance, and standard deviation.

The classification component uses the training and test datasets with a smaller sample space. The last stage is to divide the samples into two groups: healthy subjects and diseased subjects. Since a basic binary classifier will not yield the best results, an ensemble strategy using SVM.

Random Forest is chosen to create accurate predictions. When compared to existing models, the suggested EHDD model outperforms them by 98%.