Does a Random Forests machine learning algorithm accurately detect heart disease based on clinical and test data?
A Random Forests machine learning algorithm can be utilized to detect heart disease from clinical and test data to support physician decision-making.
This paper describes a method to detect possible heart disease using the Random Forests algorithm. Cardiovascular diseases are the number 1 cause of death globally - an estimated 17.9 million people died from it in 2016. This machine learning work contributes to healthcare and can detect heart disease on the basis of clinical data and test data from different patients. The result and contribution of this paper is to identify whether a patient has heart disease or not, based on the information of clinical data and test results and so support doctors in making decisions about patient treatments.
Buettner et al. (2019) studied this question.