Heart disorder has just overtaken cancer as the world's biggest cause of mortality. Several cardiac failures, heart disease mortality, and diagnostic costs can all be reduced with early identification and treatment. Medical data is collected in large quantities by the healthcare industry, but it is not well mined. The discovery of previously unknown patterns and connections in this information can help with an improved decision when it comes to forecasting heart disorder risk. In the proposed study, we constructed an ML-based diagnostic system for heart illness forecasting, using a heart disorder dataset. We used data preprocessing techniques like outlier detection and removal, checking and removing missing entries, feature normalization, cross-validation, nine classification algorithms like RF, MLP, KNN, ETC, XGB, SVC, ADB, DT, and GBM, and eight classifier measuring performance metrics like ramification accuracy, precision, F1 score, specificity, ROC, sensitivity, log-loss, and Matthews' correlation coefficient, as well as eight classification performance evaluations. Our method can easily differentiate between people who have cardiac disease and those are normal. Receiver optimistic curves and also the region under the curves were determined by every classifier. Most of the classifiers, pretreatment strategies, validation methods, and performance assessment metrics for classification models have been discussed in this study. The performance of the proposed scheme has been confirmed, utilizing all of its capabilities. In this work, the impact of clinical decision support systems was evaluated using a stacked ensemble approach that included these nine algorithms

翻译：心脏病已超越癌症成为全球首要致死病因。早期诊断与治疗可有效降低心脏衰竭发生率、心脏病致死率及诊疗成本。医疗行业虽积累了海量数据，却未能充分挖掘其价值。通过发现数据中隐藏的关联模式与未知规律，可显著提升心脏病风险预测的决策质量。本研究基于心脏病数据集，构建了基于机器学习的心脏病预测诊断系统。我们采用包括异常值检测与剔除、缺失值校验与填补、特征归一化及交叉验证在内的数据预处理技术，融合RF、MLP、KNN、ETC、XGB、SVC、ADB、DT、GBM等九种分类算法，并从分类准确率、精确率、F1分数、特异性、ROC曲线、灵敏度、对数损失和Matthews相关系数等八个维度评估分类器性能。本方法能够有效区分心脏病患者与健康人群，并通过接收者操作特征曲线及曲线下面积评估各分类器性能。研究系统探讨了多数分类器的预处理策略、验证方法及性能评估指标，全面验证了所提方案的可行性。本研究采用包含上述九种算法的堆叠集成方法，临床决策支持系统的效能评估得以实现。