Accurate Heart Disease Prediction via Improved Stacking Integration Algorithm

The stacking algorithm has better generalization ability than other learning algorithms, and can flexibly handle different tasks. The basic model of this algorithm uses heterogeneous learning devices (different types of learning devices), but for each data set in K-fold cross validation, the learners used are homogeneous (the same type of learner). Considering the neglect of the precision difference by a homogeneous heterotopic learner, the accuracy difference weighting method is proposed to improve the traditional stacking algorithm. In the first layer of the traditional stacking algorithm, the algorithm is weighted according to the prediction accuracy, that is, the output of the test set of the first layer is weighted by the weight calculated with the obtained precision, and the weighted result input into the element learner is taken as the feature. As one of the diseases with the highest incidence and mortality, the effective prediction of heart disease can provide an important basis for assisting diagnosis and enhancing the survival rate of patients. In this article, the improved stacking integration algorithm was used to construct a two-layer classifier model to predict heart disease. The experimental results show that the algorithm can effectively improve the prediction accuracy of heart disease through the verification of other heart disease data sets, and it is found that the stacking algorithm has better generalization performance.