Prediction of heart disease using machine learning and hybrid methods

Heart disease is a major cause of death worldwide, and early diagnosis and treatment can significantly improve patient outcomes. Machine learning has been shown to be a promising tool for heart disease prediction, but existing methods have limitations such as low accuracy and high computational complexity. In this research, we investigate a novel method for heart disease estimate using various machine learning. Our method is based on a combination of deep learning and ensemble learning techniques. We first use a deep learning model to extract features from the patient data. These features are then used to train an ensemble learning model, which makes the final prediction. We evaluated our method on a publicly available dataset of heart disease patients. Our method achieved an accuracy of 94%, significantly higher than the existing methods' accuracy. Our method is also more computationally efficient than existing methods, making it suitable for use in real-world applications. We believe our method can potentially improve the early detection and treatment of heart disease. We plan to evaluate our method on a larger dataset further and explore the use of our method in clinical settings.