Open AccessEmpirical Analysis of Cardiovascular Diseases using Machine Learning and Soft Computing Techniques
Author(s) -
Raghavendra Kumar,
Ashish Mishra,
Himanshu Rathore
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1494.109119
Subject(s) - soft computing , benchmark (surveying) , support vector machine , computer science , machine learning , random forest , artificial neural network , artificial intelligence , multidisciplinary approach , social science , geodesy , sociology , geography
Cardiovascular diseases are a one of the most exigent issue in healthcare domain. There have been various multidisciplinary approaches proposed and applied to reduce the mortality rate. As per literature and current study machine learning and soft computing techniques are efficient and widely accepted approaches in research community. This paper identifies and compares the various techniques of machine learning using Random Forest (RF), Support Vector Machine (SVM), XG Boost and Artificial Neural Network (ANN) and uncovers the F1 score, recall, precision to predict efficient and more accurate result. The results are further compared with existing benchmark models and showed significant improvement in heart disease prediction of patient.