An integrated decision support system for heart failure prediction based on feature transformation using grid of stacked autoencoders

Heart failure (HF) is a life-threatening disease. Therefore, prediction of risks associated with HF is essential at the early stages of the disease in order to ensure safety and reduce the mortality rate associated with it. To meet this objective, numerous medical decision support systems (MDSS) have been developed for HF risk prediction. Most of these MDSS focus on feature transformation-based methods and their integration with machine learning models. However, careful investigation of these MDSS reveals that they improved the accuracy on the testing data while the performance on the training data was either compromised or not taken into account. Such an improvement is not pertinent from machine learning perspective. Furthermore, the produced HF prediction accuracy also needs considerable amount of improvement. To tackle such issues, this study proposes a more robust approach that integrates a grid of stacked autoencoders with neural network model. The stacked autoencoders transform features by yielding more robust set of features and the neural network classifies the newly extracted set of features. The strength of the proposed integrated MDSS is evaluated by using an online HF benchmark database namely Cleveland database. The performance of the proposed feature transformation method is also compared with other well known methods namely Principal Component Analysis (PCA), Kernel PCA (KPCA) and feature selection methods i.e., chi-square, Gini index, F-score and mutual information based feature selection. The proposed approach outperformed other state-of-the-art feature transformation methods. The proposed integrated MDSS achieves classification accuracy of 95.55%, specificity of 97.95%, and sensitivity of 92.68%.

Automated summary

Heart failure is a life-threatening disease, and it is important to predict associated risks early on. Existing medical decision support systems have focused on feature transformation methods and machine learning models, but there are issues with performance and accuracy. This study proposes a more robust approach that integrates stacked autoencoders with a neural network model to improve classification accuracy.