Pathological prediction of heart disease based on deep width learning algorithm

Abstract

As the technology has become more and more advance and massive data has been produced and pre-processed became useful information to become big data so come out a lot of technology like data mining, neural network, deep learning, etc. Each technology has its advantages, this paper put forward the width of the depth of the learning algorithm is to the advantage of two types of deep learning methods This new method is combining of Flexible Neural Tree(FNT) and Broad Learning System(BLS) to become a new deep width network. After several times of experiments, we can confirm that this new hybrid method can increase the accuracy, sensitivity, and specificity of Heart disease prediction. Therefore, having higher accuracy of prediction for heart disease means we can notice more people before having heart disease and start to prevent it and in the end, we can prevent loss of life and economy.