Cardiovascular diseases (CVDs) are the most perilous
disease in worldwide. The CVD risk occurs abruptly at any instance
with or without symptoms. The World Health Organization data
shows CVD is the maximum rated disease. Artificial intelligence
plays significant role for early diagnosis of CVD that supports health
care professions. Advanced machine and deep learning techniques
enhance the prediction of CVD based on clinical data. We propose
MLP learning and testing model as an ensemble technique for the
prediction of CVD risk. This ensemble model has two phases
namely learner phase as level 0 and meta phase as level 1. Training
is performed in learner phase using Logistic Regression, Decision
Tree, Naïve Bayes, Support Vector Machine, and Random Forest
and Multilayer perceptron. Multilayer perceptron is used in meta
phase to test the model. The ensemble model is trained using 2215
samples of UCI dataset and tested with multilayer perceptron using
443 samples. The proposed model attains an accuracy rate of 96.1
%. The proposed model is then compared with existing CNN and
ANN models and outperforms the other models.