CARDIAC DISEASE PREDICTION USING MACHINE LEARNING ALGORITMS

Abstract

This study focuses on the application of machine learning methods for predicting cardiac diseases, aiming to develop accurate and interpretable diagnostic models. The primary objective is to enhance prediction accuracy and identify key risk factors. Various classification algorithms, including Random Forest, Gradient Boosting, Support Vector Machines (SVM), and Neural Networks, are utilized. A dataset containing clinical attributes of patients is processed and analyzed to build predictive models. The model performance is evaluated using accuracy metrics, confusion matrices, and ROC-AUC scores. The results indicate that the Random Forest algorithm achieves the highest predictive accuracy. Furthermore, feature importance analysis highlights the most significant factors contributing to the development of cardiac diseases. This research provides valuable insights for medical professionals and researchers, offering effective tools for early disease detection.