Comparison of the effects of features and classifiers on performance in the cardiovascular disease detection system

This study aims to analyze the effects of features and classifiers in detecting cardiovascular diseases (CVD), which remain the leading cause of morbidity and mortality worldwide. Early and accurate detection of CVD significantly affects treatment outcomes. Therefore, the proposed method aims to automatically detect cardiovascular diseases via artificial intelligence. In this research, the performances of artificial intelligence methods for the cardiovascular disease detection problem are analyzed. The dataset used in this study was sourced from the publicly available Kaggle platform. It used for performance analysis in the developed application includes the features of 70000 patients such as age, gender, height, weight, blood pressure, cholesterol, glucose, smoking and alcohol use. These features were classified with Gradient Boosting, XGBoost, SVM, Random Forest, Logistic Regression, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) methods and performance comparison was performed. In the experimental results, the highest accuracy rate of 72.55% was obtained using the Gradient Boosting method, demonstrating its superior performance in cardiovascular disease detection. In addition, it was observed that the classification performance decreased when the high blood pressure attribute was removed from the dataset, while the removal of other features did not significantly affect the performance.