Permutation Feature Importance-Based Cardiovascular Disease (CVD) Prediction Using ANN

Diseases affecting heart and blood arteries are often referred to as cardiovascular diseases. It is crucial to predict and diagnose accurately cardiovascular disease in order to provide proper treatment. For detection of hidden patterns of cardiovascular diseases from massive amounts of data, deep learning techniques are commonly used. Different predictive models are used in health care to anticipate patient’s future ailments by learning from their prior data. This study seeks to create an intelligent agent for making such predictions automatically. This research mainly contributed to discover the most important attributes that best define the relationship with the target attribute and to find a proper algorithm for the prediction of cardiovascular disease. Using methods like Pearson correlation analysis and permutation feature importance along with effective data preprocessing techniques, the most crucial characteristics of cardiovascular diseases have been determined. A consolidated dataset from IEEEDataPort was used in this study. This study achieved 95% accuracy in predicting cardiac disease using Artificial Neural Network (ANN). This research will help healthcare people to predict a person’s cardiovascular disease at early stage. It will also help researchers discover more ways to anticipate cardiovascular illnesses.