Simulation Studies in Heart Disease Prediction by Machine Learning Techniques

Simulation Studies in Heart Disease Prediction by Machine Learning Techniques

Risha Ahmed1, Dr. Anurag Jain2

1 Research scholar, Computer Science & Engineering, REC, Bhopal, M.P

2 Director Computer Science & Engineering, Radharaman group , Bhopal, M.P

Abstract

Heart disease remains a leading cause of mortality worldwide, highlighting the need for early detection and accurate diagnosis to improve patient outcomes. This research investigates the use of various machine learning algorithms, including Sequential Minimal Optimization (SMO), Random Forest, Naive Bayes, and Random Tree, to predict cardiovascular diseases. By leveraging the Coronary Heart Disease Database (CHDD), we aim to develop a predictive model that utilizes historical patient data to identify risk factors and patterns associated with heart disease. Our study compares the performance of these machine learning algorithms to determine their effectiveness in accurately classifying and predicting heart disease. The results demonstrate that these methods can significantly enhance diagnostic accuracy, supporting better clinical decision-making and timely interventions. Specifically, the Random Forest algorithm showed superior performance in terms of prediction accuracy, while SMO and Naive Bayes provided valuable insights into different aspects of the data. This research underscores the potential of machine learning in transforming cardiovascular disease diagnosis and management. By highlighting the effectiveness of individual algorithms, our study contributes to the development of reliable and robust diagnostic tools, aiming to reduce the global burden of heart disease.

Keywords: Machine Learning,  Heart disease, Predictive model