Heart Disease Prediction Using CNN with Various Feature Selection Approaches

Objectives: To evaluate the performance of CNN models with feature selection methods like Relief, Uniform Manifold Approximation and Projection (UMAP), and Linear discriminant Analysis (LDA) for forecasting heart diseases. Methods: The present research for heart disease prediction compares the performance of feature selection algorithms like ReliefF, Uniform Manifold Approximation and Projection (UMAP), and Linear discriminant Analysis (LDA) with Convolution Neural networks (CNN) for prediction. The study is conducted in a dataset with 303 records collected from patients with 14 attributes. It is also validated with the publicly available Cleveland dataset (Kaggle). The software environment used for implementation is Jupyter Notebook, which uses Python. The dataset consists of 303 records collected from the patients with 14 attributes. It is validated with the publicly available Cleveland dataset. Findings: The study examines how these feature selection techniques affect CNN's accuracy. According to experimental findings, the CNN-UMAP hybrid model outperforms with an accuracy of 91.88%, precision of 0.89, and recall of 0.85 compared to ReliefF and LDA, UMAP shows up as the most successful feature selection method among the studied techniques when utilized alongside CNNs. Novelty: ReliefF, UMAP, and LDA allow CNN to learn more significant and discriminative features by minimizing the dimensions of the input data while preserving its underlying pattern. Previous studies also attempted to identify the key contributing characteristics to heart disease prediction, but less emphasis was placed on these feature selection methods in determining the effectiveness of the features for heart disease prediction. Keywords: Heart Disease, Feature Selection Methods, Convolutional Neural Network, ReliefF, UMAP and LDA