Abstract
Detecting cardiovascular problems during their early stages is one of the great difficulties facing physicians. Cardiovascular diseases contribute to the deaths of around 18 million patients every year worldwide. That's why heart disease is a critical worry that must be addressed. However, it can be difficult to detect heart disease because of the multiple factors that affect health, such as high blood pressure, increased cholesterol, abnormal pulse rate, and many other factors. Therefore, the field of artificial intelligence can be instrumental in detecting diseases early on and finding an appropriate solution. This paper proposes a model for diagnosing the probability of an individual having cardiovascular illness by employing Machine Learning (ML) models. The experiments were executed using seven algorithms, and a public dataset of cardiovascular disease was used to train the models. A Chi-square test was used to identify the most important features to predict cardiovascular disease. The experiment results showed that Multi-Layer Perceptron gives the highest accuracy of disease prediction at 87.23%.