Risk Assessment of Cardiovascular Diseases Using kNN and Decision Tree Classifier

Abstract

As per the information provided by WHO, most of the people die from the cardiovascular disease. In 2019, almost 32% of the global deaths were due to cardiovascular diseases, out of which 85% were because of heart attacks and strokes. Hence, it is very important to predict the chances and risk of cardiovascular disease event, to prevent any damage in future. Cardiovascular diseases are the disorders of blood vessels supplying blood to heart, brain and different parts of the body. There are different causes of cardiovascular diseases, which can be quantified with the help of different features and with supporting attributes like age of the person, any diseases like diabetes, blood pressure etc., the risk of the cardiovascular disease can be assessed to prevent the further losses. Machine learning approach is very useful in these circumstances, where quantified data values are available in terms of data set. Machine learning techniques can be used to find the risk of cardiovascular disease. Here, we are proposing to use the two machine learning classifiers such as kNN and decision tree. kNN helps us to find the possibility of cardiovascular disease and decision tree helps us to classify the type of the cardiovascular disease with the risk involved. This approach is very useful, as decision tree is one of the most accurate classifiers, which also helps us to identify the specific cardiovascular disease that can be the future event based on feature values. This proposed methodology is justified with proper research gap stating the important of the proposed architecture and implementation results, which gives effective way for assessing the risk of cardiovascular disease.