Prediction of cardiovascular Disease Using Classification Techniques With High Accuracy

P Siva Kumar, Dr.N.Anbazhaghan, Shaik Razia, M Sivani, S Pravalika, A S Harshini

Cardio-vascular disease now-a-days have become very much common in healthcare industries. The ages of people suffering from these diseases ranges from10 year’s old children to 60 year old man. In supervised learning classification techniques are playing major role in data-mining. Classification is one of the emerging data-mining techniques. The main idea of classification algorithm is to keep the data in the suitable class. The classification techniques used here are logistic, SVM, KNN, extra tree classifier, bagging classifier, gradient classifier, Adabooster classifier, Random fore-stand xgb classifier. The aim of this project is to find the best classification technique which gives high accuracy. Here we are using python (Anaconda-jupyter) in order to get the best classification technique for the prediction of cardio-vascular disease. The classification algorithm which gives the best accuracy will be considered as the best algorithm for the prediction of the heart disease.

Volume 12 | Issue 2

Pages: 1134-1139

DOI: 10.5373/JARDCS/V12I2/S20201145