Prediction of Cardiovascular Disease using Machine Learning

Abstract

Machine learning is a technique converts the raw clinical data into an informational data that helps for decision making and prediction. Cardiovascular disease is one of the major causes of mortality around the world. It is considered in a large scale, so prediction of cardiovascular disease is more important in the clinical survey analysis as day by day it gets increased. The amount of data in the health club is huge. As cardiovascular is one of the major causes for death there are some data analytical techniques that predicts the occurrence of cardiovascular disease. It can be achieved through selecting a correct combination of prediction models and features. Prediction models were developed using different classification techniques based on feature selection and there are certain algorithms which provide varied and improved accuracy. Here prediction model is developed using Random Forest classification technique - Method for classification, regression by constructing a multitude of decision trees at training time. Developed by aggregating tree Avoids over fitting can deal with large number of features. Helps with feature selection based on importance where necessary features only classified. Pre-processing will be done first considering the clinical data. It will be spited into train and test data with which accuracy can be achieved.