Accurate and Predictable Cardiovascular Disease Detection by Machine Learning

Abstract

The first step in treating a disease is identifying and predicting it in patients. In the area of at-risk patient detection,
we evaluate machine learning algorithms and identify important variables in the data that lead to this disease. To
match with the other sectors, there is a large amount of data in the health sector that could be used to deal with
various illnesses. One of the rising health concerns is cardiovascular disease that can be effectively treated if it is
detected early on. The use of ML algorithms is essential for this purpose. The different algorithms for machine
learning, as well as the various features that can be used to train these algorithms for cardiovascular detection, have
all been discussed. In our survey questionnaire analysis, we have demonstrated that automated identification
mechanisms for patients at risk of diabetes and cardiovascular diseases can be built using machine learning models.
Additionally, we also identify key contributors to the prediction, which can be further investigated for their possible
consequences on electronic health records.