Enhancing Cardiovascular Risk Prediction with an Ensembled Model Approach

Accurate risk assessment methods are crucial for early intervention and prevention, as cardiovascular diseases remain the leading cause of mortality and disability worldwide. With the advent of deep-learning techniques, the creation of predictive models using extensive medical data has significantly increased. This article provides a comprehensive overview of current deep learning applications for cardiovascular risk prediction. It begins with a summary of conventional risk assessment techniques and their shortcomings, underscoring the need for more sophisticated predictive models. So, in this article we discussed the use of ensembled techniques for predicting cardiovascular risk, and highlighting the need for more sophisticated models and also the limitations of conventional risk assessment methods which in turn tells the need for more sophisticated predictive models. The study proposes a heart attack prediction system using ensembled model along with data mining, achieving reliable results with minimal error rates. The study also introduces a novel classifier called the Ensembled Cardiovascular Disease Prediction Classifier (ECVDPC), outperformed when compared with base classifiers like Naïve Bayes and Decision Tree i.e., 96%.