Abstract

Info

Title:: Heart disease prediction using IHB-LightGBM model based on incomplete?ata

Author(s):: ZHAO Xiaoqiang1; 2; 3; QIAO Hui1; 1. School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China 2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, China 3. National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050, China

Keywords:: Keywords: data mining heart disease prediction hyperparameter optimization algorithm LightGBM algorithm

Abstract:: Abstract: An improved Hyperband-light gradient boosting machine (IHB-LightGBM) model for heart disease prediction based on incomplete data is presented in the study. Based on the sampling of Hyperband algorithm for hyperparameter estimation, the weight values are introduced, and the reservoir method is used to sort the parameters according to the feature weights, so as to screen out the optimal parameters to improve the parameter optimization ability of the algorithm. Subsequently, to overcome the problems of small sample size and missing attributes of heart disease data, K-nearest neighbor algorithm is used to perform the interpolation of missing values for incomplete data, and the obtained complete data are normalized and mapped to the range from 0 to 1. The IHB optimization algorithm is adopted for global parameter optimization, and the IHB-LightGBM model for heart disease prediction is established. Experiments are carried out using the UCI heart disease data sets, and the results show that IHB algorithm is superior to Bayesian, random search and other optimization algorithms in parameter optimization, and that the various evaluation indicators of IHB-LightGBM model are significantly higher than those of random forest, extreme random tree and other algorithms. The proposed model improves both efficiency and accuracy of prediction.