Analysis of Heart Disease Prediction Algorithms

This paper investigates the accuracy of three machine learning algorithms for heart disease prediction: convolutional neural networks (CNNs), XGBoost, and logistic regression. The experiment was conducted on a dataset from Kaggle using Google Colab. All three algorithms achieved good results, but the CNN outperformed the others with an accuracy of 93.7%. Logistic regression followed with 83.46% accuracy, and XGBoost achieved 80.31% accuracy. The findings suggest that CNNs are a powerful tool for heart disease prediction, even when compared to established algorithms like logistic regression and XGBoost