Heart disease prediction system

Abstract

[1]According to the World Health Organization (WHO), 17.9 million people die each year due to cardiovascular diseases (CVDs), almost 31% of all deaths worldwide. This single piece of evidence is strong enough to describe the lethal nature of cardiovascular diseases or, as we know, heart diseases. There is no denying that different medical sectors using the help of high-end technologies, now have gured out ways to tackle serious CVDs. However, then again, we indeed cannot rule out the amount of distress these CVDs bring. We need to know how to prepare ourselves to face di erent heart diseases. One of the many ways can be implementing di erent Machine Learning and Neural Network algorithms. Say, for example, in this paper; we will discuss algorithms like Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), ConvMLP, and ANN; on how each of these techniques can be applied to nd out a better way to predict the availability of heart disease in a particular individual depending on few given factors. Our main goal is to make the course easy to detect diseases that belong to the heart and enriches the medical sector. In our country, the medical sector is improving day by day. We aim to boost this improved significantly. By using Machine Learning and Neural Network algorithm, we are optimistic about implementing this idea.