Paper Title
Cardiovascular Disease Prediction using Machine and Deep learning techniques

Abstract
The most serious cardiac conditions are known as cardiovascular diseases (CVDs). It is crucial to have precise analytics for cardiac disease in real-time. Our goal was to take a close look at how well and how practical popular ML and deep learning algorithms are for predicting the probability of CVD. In order to analyse user data and provide real-time predictions of CVDs, the smart application included deep learning or machine learning algorithms. We trained and tested the deep and machine learning approaches using a popular open-access dataset. This study provides a performance evaluation of models for cardiovascular disease prediction using data from well-known datasets such as the Cleveland dataset and the Z-Alizadeh Sani dataset. Heart disease prediction using machine learning (ML) and deep learning (DL) is the focus of this article. Machine learning has the potential to be a real alternative diagnostic tool for prognosis, which will keep patients informed about their health status. Additional investigation into the most effective prediction models is required, as, despite the researcher's best efforts, there is still room for uncertainty about the standardization of prediction models. Keywords - Cardiovascular Diseases, Machine Learning, Deep Learning, Features Selection, Prediction