The dataset comprises more than 5,000 observations of 12 attributes representing patients’ clinical conditions like heart disease, hypertension, glucose, smoking, etc. For each instance, there’s also a binary target variable indicating if a patient had a stroke.
We can build a model to predict the occurrence of a stroke by training typical classification algorithms, for example, Logistic Regression, K-Nearest Neighbors, Support Vector Machines classifiers, Decision Trees, or others.
Of course, the actual applicability of such a model depends on how representative the patients dataset is. However, this is a good exercise for those who wish to understand how to apply machine learning in healthcare.