Abstract
In the world, cardiovascular disease is a common, debilitating disease that affects human lives in many different ways. It is important to detect heart disease effectively and reliably in order to avoid potential heart failures. Since heart failure is a major risk, it should be successfully treated in the early stages of heart disease. Many programs exist that can diagnose heart disease at an early stage using machine learning techniques. But these predictive systems are difficult to predict the heart conditions accurately with a minimum of time. Today, the stochastic gradient boosting with recursive feature elimination approach has been developed for feature selection. The results of the clustering were based on the adaptive Harris Hawk optimization algorithm. The selected features allowed us to better identify people with heart disease because they all have the same features. Classification is achieved using an improved deep genetic algorithm (EDGA). The system enhances the DNN's initial weights by using an augmented genetic algorithm and proposing the best initial weights for the DNN using neural network. The technique is illustrated using the publicly accessible dataset from the UCI machine learning repository. The study found that EDGA is well suited for predicting heart disease.