Multi-view ensemble learning with empirical kernel for heart failure mortality prediction

Heart failure (HF) refers to the heart's inability to pump sufficient blood to maintain the body's needs, which has a very serious impact on human health. In recent years, the prevalence of HF has remained high. This paper proposes a multi-view ensemble learning algorithm based on empirical kernel mapping called MVE-EK, which predicts the mortality of patient through hospital records. Multi-view ensemble learning can take advantage of the consistency and complementarity of different views. The MVE-EK first divides the patient's features into multiple views and then divides the samples of each view to multiple subsets through under sampling, which can reduce the imbalance rate of the original dataset and obtain some relatively balanced subsets. Each subset is mapped into kernel space by empirical kernel mapping, which can map samples from linearly inseparable spaces to linearly separable spaces. Finally, the multi-view ensemble learning is performed by the designed loss of acquaintance between views. The effectiveness of the algorithm is verified on the three datasets of HF patient in the real world. The performance of the algorithm is better than other comparison algorithms. The datasets are collected from Shanghai Shuguang Hospital and involve 10 203 hospitalization records for 4682 HF patients between March 2009 and April 2016. The prediction information provided by the algorithm can assist the clinician in providing a more personalized treatment plan for patients with HF.