Available clinical methods for heart failure (HF) diagnosis are expensive and require a high-level of experts intervention. Recently, various machine learning models have been developed for the prediction of HF where most of them have an issue of over-fitting. Over-fitting occurs when machine learning based predictive models show better performance on the training data yet demonstrate a poor performance on the testing data and the other way around. Developing a machine learning model which is able to produce generalization capabilities (such that the model exhibits better performance on both the training and the testing data sets) could overall minimize the prediction errors. Hence, such prediction models could potentially be helpful to cardiologists for the effective diagnose of HF. This paper proposes a two-stage decision support system to overcome the over-fitting issue and to optimize the generalization factor. The first stage uses a mutual information based statistical model while the second stage uses a neural network. We applied our approach to the HF subset of publicly available Cleveland heart disease database. Our experimental results show that the proposed decision support system has optimized the generalization capabilities and has reduced the mean percent error (MPE) to 8.8% which is significantly less than the recently published studies. In addition, our model exhibits a 93.33% accuracy rate which is higher than twenty eight recently developed HF risk prediction models that achieved accuracy in the range of 57.85% to 92.31%. We can hope that our decision support system will be helpful to cardiologists if deployed in clinical setup.

可用的心力衰竭 (HF) 诊断临床方法昂贵且需要高水平的专家干预。最近，已经开发了各种机器学习模型来预测 HF，其中大多数模型都存在过度拟合的问题。当基于机器学习的预测模型在训练数据上表现出更好的性能，但在测试数据上表现出较差的性能时，就会发生过度拟合，反之亦然。开发一个能够产生泛化能力的机器学习模型（使模型在训练和测试数据集上表现出更好的性能）可以总体上最小化预测误差。因此，此类预测模型可能有助于心脏病专家有效诊断 HF。本文提出了一种两阶段决策支持系统来克服过拟合问题并优化泛化因子。第一阶段使用基于互信息的统计模型，而第二阶段使用神经网络。我们将我们的方法应用于公开可用的克利夫兰心脏病数据库的 HF 子集。我们的实验结果表明，所提出的决策支持系统优化了泛化能力，并将平均百分比误差 (MPE) 降低到 8.8%，明显低于最近发表的研究。此外，我们的模型具有 93.33% 的准确率，高于最近开发的 28 个 HF 风险预测模型，准确率在 57.85% 到 92.31% 之间。