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.

在世界范围内，心血管疾病是一种常见的令人衰弱的疾病，它以多种不同的方式影响着人们的生活。重要的是要有效可靠地检测心脏病，以避免潜在的心力衰竭。由于心力衰竭是主要风险，因此应在心脏病的早期阶段对其进行成功治疗。存在许多可以使用机器学习技术在早期诊断出心脏病的程序。但是这些预测系统很难在最短的时间内准确地预测心脏状况。如今，已经开发出具有递归特征消除方法的随机梯度增强技术来进行特征选择。聚类的结果基于自适应哈里斯·霍克（Harris Hawk）优化算法。选定的特征使我们能够更好地识别心脏病患者，因为他们都有相同的特征。使用改进的深度遗传算法（EDGA）实现分类。该系统通过使用增强的遗传算法来增强DNN的初始权重，并使用神经网络为DNN提出最佳的初始权重。使用UCI机器学习存储库中可公开访问的数据集说明了该技术。研究发现EDGA非常适合预测心脏病。使用UCI机器学习存储库中可公开访问的数据集说明了该技术。研究发现EDGA非常适合预测心脏病。使用UCI机器学习存储库中可公开访问的数据集说明了该技术。研究发现EDGA非常适合预测心脏病。