Reducing bias in coronary heart disease prediction using Smote-ENN and PCA.

ABSTRACT: Coronary heart disease (CHD) is a major cardiovascular disorder that poses significant threats to global health and is increasingly affecting younger populations. Its treatment and prevention face challenges such as high costs, prolonged recovery periods, and limited efficacy of traditional methods. Additionally, the complexity of diagnostic indicators and the global shortage of medical professionals further complicate accurate diagnosis. This study employs machine learning techniques to analyze CHD-related pathogenic factors and proposes an efficient diagnostic and predictive framework. To address the data imbalance issue, SMOTE-ENN is utilized, and five machine learning algorithms-Decision Trees, KNN, SVM, XGBoost, and Random Forest-are applied for classification tasks. Principal Compone

SUBMITTER: Wei X 

PROVIDER: S-EPMC12331108 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature