Comparison of dimensionality reduction methods and evaluation of prediction effect based on cardiovascular disease data

Abstract

Cardiovascular diseases have become the leading cause of death in the world, and the mortality rate caused by them is still on the rise, which is a major challenge facing the world. In the study of cardiovascular diseases, there are many kinds and quantities of factors leading to the disease, so how to screen more effective factors for research and accurate prediction is an important problem. The aim of this study is to conduct an in-depth comparative analysis of multiple dimensionality reduction methods for cardiovascular disease data. This paper evaluates the results produced by various dimensionality reduction methods and models, and uses Accuracy Rate, Confusion Matrix and Area Under Curve (AUC) and other indicators to evaluate their prediction effects. The study finds that the decision tree model has the best performance and is superior to LDA linear discriminant analysis in terms of accuracy and data dimension. The principal component analysis method is relatively complex, although it can effectively reduce the data dimension, its accuracy in forecasting decreases, and this method is not conducive to horizontal and vertical comparison and the accumulation of statistical data.