A Comprehensive Review of Recent Advances in Heart Disease Prediction using Machine Learning Algorithms with Optimization Techniques and Feature Selection

The biggest challenge with heart disease is its detection. There are various tools available that can predict the heart disease; but they are expensive when analysing the chances of heart disease in humans. Early prediction of heart disorders helps reduce the mortality rate and other problems. In this modern world, we have huge amount of data, therefore a variety of machine learning approaches can be used to detect heart disease accurately and efficiently as well. This review paper aims to provide an overview of the current state of Machine learningbased heart disease prediction, including the various Machine learning techniques and models that have been used, the data sources, optimization techniques, feature selection methods employed, and the performance of these models in terms of accuracy. Overall, this review paper will provide a comprehensive overview of the current state of ML-based heart disease prediction and help guide future research in this field