Heart disease is a serious global health issue that claims millions of lives every year. Early detection and precise prediction are critical to the prevention and successful treatment of heart related issues. A lot of research utilizes machine learning (ML) models to forecast cardiac disease and obtain early detection. In order to do predictive analysis on "Heart disease health indicators " dataset. We employed five machine learning methods in this paper: Decision Tree (DT), Random Forest (RF), Linear Discriminant Analysis, Extra Tree Classifier, and AdaBoost. The model is further examined using various feature selection (FS) techniques. To enhance the baseline model, we have separately applied four FS techniques: Sequential Forward FS, Sequential Backward FS, Correlation Matrix, and Chi2. Lastly, K means SMOTE oversampling is applied to the models to enable additional analysis. The findings show that when it came to predicting heart disease, ensemble approaches in particular, random forests performed better than individual classifiers. The presence of smoking, blood pressure, cholesterol, and physical inactivity were among the major predictors that were found. The accuracy of the Random Forest and Decision Tree model was 99.83%. This paper demonstrates how machine learning models can improve the accuracy of heart disease prediction, especially when using ensemble methodologies. The models provide a more accurate risk assessment than traditional methods since they incorporate a large number of factors and complex algorithms.

翻译：心脏病是严重的全球性健康问题，每年导致数百万人死亡。早期检测与精准预测对于预防和成功治疗心脏相关问题至关重要。大量研究利用机器学习模型预测心脏疾病以实现早期检测。为对"心脏病健康指标"数据集进行预测分析，本文采用五种机器学习方法：决策树、随机森林、线性判别分析、极端树分类器和AdaBoost。通过多种特征选择技术对模型进行进一步检验。为提升基线模型性能，我们分别应用了四种特征选择技术：顺序前向选择、顺序后向选择、相关矩阵法和卡方检验法。最后，对模型应用K均值SMOTE过采样以支持补充分析。研究结果表明，在心脏病预测任务中，集成方法（特别是随机森林）的表现优于独立分类器。吸烟状况、血压、胆固醇水平和缺乏体育锻炼被识别为关键预测因子。随机森林与决策树模型的准确率达到99.83%。本文论证了机器学习模型如何提升心脏病预测精度，特别是在采用集成方法时。由于整合了大量影响因素和复杂算法，这些模型相比传统方法能提供更精确的风险评估。