Integrating Canonical Correlation Analysis with Random Forest for Heart Disease Prediction

Heart disease, a leading global cause of death over the past several decades, encompasses a range of disorders affecting the heart. Researchers use various data mining and machine learning techniques to analyze complex medical data, aiding healthcare professionals in predicting cardiac conditions. Despite these advances, existing models often struggle with effectively modelling non-linear relationships, maximizing feature correlation, and addressing challenges related to dimensionality and overfitting. This research paper introduces the Hybrid CCRF model for heart disease prediction, which integrates Canonical Correlation Analysis (CCA) with Random Forest. The proposed model generates polynomial features to capture non-linear relationships and applies Canonical Correlation Analysis to identify canonical variables that maximize correlations between heart disease features and chronic condition features. By combining these canonical variables into a single feature set, the model enhances prediction accuracy. The objectives of the Hybrid CCRF model are threefold: 1) To capture complex non-linear relationships between heart disease and chronic condition features by integrating polynomial feature generation with Canonical Correlation Analysis, thereby improving the model’s ability to represent intricate data patterns; 2) To use CCA to identify and integrate canonical variables that enhance feature correlation, creating a more informative feature set; and 3) To address high-dimensional data and overfitting issues by combining canonical variables with polynomial features in a Random Forest model, balancing complexity and performance for improved generalization and robustness across various datasets. The proposed model achieved an accuracy of 99.45%, with a sensitivity of 98.53%, specificity of 99.54%, precision of 95.73%, and an F1 Score of 0.9711, outperforming all existing models.