Prediction of cardiovascular diseases using weight learning based on density information

Abstract

The uneven distribution of sample points is a common problem in medical datasets. How to improve the classification accuracy with these datasets remains to be solved. Based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm, a weight learning approach is proposed to utilize the density information of datasets for the accurate prediction of cardiovascular diseases (CVDs). The approach selects important features by the random forest (RF) algorithm, divides the sample points into three types and weights them using different values by weight learning based on the density. Thus, the constructed machine learning models that combine the original features and weight feature can learn density information, more effectively identify decision boundaries, and achieve better performance. Compared with conventional machine learning models, the cross-validation approach showed that the performance of machine learning models with weight learning could achieve improved accuracy by 3 percentage points with the Stroke dataset and more than 10 percentage points with the University of California, Irvine (UCI) dataset.