Abstract

Machine learning model predicting the likelihood of a patient developing cardiovascular disease based on their medical history and risk factors.

Author(s): N John Camm, Semi Redzeppagc, Ivan Ilic, Adnan Raufi, Mario Iannaccone, Udi Nussinowitch, Su Hnin Hlaing

Cardiovascular disease (CVD) is a leading cause of death and disability worldwide, and early identification
of individuals at high risk of developing CVD can help to prevent or mitigate the impact of these conditions.
Machine learning algorithms have been developed to predict the likelihood of an individual developing
CVD based on their medical history and other risk factors. One approach to using machine learning for CVD
risk prediction is to train a model on a large dataset of patients with and without CVD, along with their
relevant risk factors and medical history. The model can then use this training data to identify patterns that
are associated with an increased risk of CVD. There are several potential benefits to using machine learning
for CVD risk prediction. For example, these algorithms can help to identify individuals who may be at high
risk of developing CVD, even if they have not yet developed any symptoms. This can allow for earlier
intervention and preventive measures, which can help to reduce the overall burden of CVD. It is important
to note that machine learning algorithms are not a substitute for clinical judgment, and should be used as a
tool to support the work of healthcare professionals. It is also important to ensure that the algorithms are
thoroughly tested and validated before they are used in clinical practice. Machine learning models have
proven to be a valuable tool in predicting the likelihood of a patient developing cardiovascular disease
(CVD) based on their medical history and risk factors. These models leverage large amounts of data and
complex algorithms to make predictions with high accuracy, providing healthcare providers with valuable
information for early intervention and improved patient outcomes. However, there is still much work to be
done to fully realize the potential of machine learning for CVD prediction, including the need for increased
data quality, advanced algorithm development, and consideration of the broader implications of using
these models. This article will provide an overview of the current state of the field and future directions for
machine learning in CVD prediction.