Heart Failure Prediction Based on Broad Learning System

Abstract Heart failure is a prevalent and serious cardiovascular condition characterized by the heart’s inability to pump blood to meet the body’s demands adequately. Current research on heart failure prediction primarily relies on conventional clinical assessment methods, traditional machine learning techniques, and traditional deep learning methods. Efficient and accurate heart failure prediction is a significant challenge due to its complex and multifactorial nature. In this study, we propose a heart failure prediction approach utilizing a broad learning system (BLS) that has the potential to capture intricate patterns in the data and enhance prediction accuracy. To evaluate our approach, we utilize an extensive dataset compiled from five previous independent datasets from the Cleveland, Hungary, Switzerland, Long Beach VA Hospital, and Stalog (heart) datasets. Experimental results demonstrate the effectiveness and efficiency of the BLS model, with a training time of 0.36 seconds and testing accuracy of 90%, precision of 88%, recall of 96%, and specificity of 82%, showcasing its potential performance for accurate heart failure prediction.