Enhanced Evolutionary Feature Selection and Ensemble Method for Cardiovascular Disease Prediction

Cardiovascular Disease (CVD) is one among the main factors for the increase in mortality rate worldwide. The analysis and prediction of this disease is yet a highly formidable task in medical data analysis. Recent advancements in technology such as Big Data, Artificial Intelligence and the need for automated models have paved the way for developing a more reliable and efficient model for predicting heart disease. Several researches have been carried out in predicting heart diseases but the focus on choosing the important attributes that play a significant role in predicting CVD is inadequate. Hence the choice of right features for the classification and the diagnosis of the heart disease is important. The core aim of this work is to identify and select the important features and machine learning methodologies that can enhance the prediction capability of the classification models for accurately predicting CVD. The results show that the proposed enhanced evolutionary feature selection with the hybrid ensemble model outperforms the existing approaches in terms of precision, recall and accuracy. The experimental outcomes show that the proposed approach attains the maximum classification accuracy of 93.65% for statlog dataset, 82.81% for SPECTF dataset and 84.95% for coronary heart disease dataset. The proposed classification model performance is demonstrated using ROC curve against state-of-the-art methods in machine learning.