A highly accurate prediction for heart failure disease: a new deep attentive model with guided feature ranking

Abstract

Abstract Heart failure (HF) is a life-threatening disease affecting at least 64 million people worldwide. Hence, it places great stresses on patients and healthcare systems. Accordingly, providing a computerized model for HF prediction will help in enhancing diagnosis, treatment, and long-term management of HF. In this paper, we introduce a new guided attentive HF prediction approach. In this method, a sparse-guided feature ranking method is proposed. Firstly, a Gauss–Seidel strategy is applied to the preprocessed feature pool for low-rank approximation procedure with a trace-norm regularization. The resultant sparse attributes, after a Spearman ranking elimination, are employed to guide the original feature pool through linear translation-variant model. Then, a fast Newton-based method is employed for a non-negative matrix factorization for the guided feature pool. The resultant bases of the factorization process are finally utilized in the adopted deep attentive predictive model. For the final prediction stage, instead of the commonly used machine learning approaches, we introduce an attentive-based classifier. It employs sequential attention to choose the most proper salient features for efficient interpretability and learning process. For the evaluation of the proposed HF prediction model, three different datasets are employed, i.e., UCI, Faisalabad, and Framingham datasets. Compared to state-of-the-art techniques, the proposed approach outperforms their performance on all datasets with even small feature sizes. With only four feature bases, the proposed method achieves an average accuracy of 98%, while, with full feature bases, full accuracy is gained.