Heart Disease Classification Based on Hybrid Ensemble Stacking Technique

Heart diseases are considered one of the leading death
rates for humanity in the recent decades. The early diagnosis and
prediction of heart disease becomes a critical subject in medical
domain. Data mining techniques are usually used for finding
anomalies, patterns and correlations within large data sets, thus it's
crucial for clinical data analysis and various disease prediction.
Ensemble approaches have proven to be quite effective in solving a
variety of classification problems. In this research, we propose a
hybrid ensemble stacking model with different feature engineering
algorithms. The proposed ensemble model is based on five base
models: Random Forest, Decision Tree, K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Naïve Bayes for heart
disease diagnosis. Logistic Regression meta model is used to merge
base models predictions. We have examined various feature selection
approaches such as: Brute Force, Principal Component Analysis
(PCA), Classification and Regression Tree (CART) Feature
Importance, and Logistic Regression based Recursive Feature
Elimination. The proposed approach has been experimentally
validated and evaluated on different dataset : UCI Cleveland and
UCI Statlog. A quantitative evaluation shows that the combination
of the ensemble model with brute force as feature selection technique
yields a top accuracy of 97.8% for heart disease classification. the
proposed stacking model has proven it's efficiency and overcomes
existing approaches in heart diseases classification