A Novel Technique for Prediction of Cardiovascular Disease

Globally heart disease is one among the major causes of death. As heart plays a major role in making sure every cell of the body gets oxygenated blood hence any problem in heart will result in major issues in human body and chances of death are quite high. It is observed that there are multiple datasets involved in testing with multiple attributes in each dataset. Each attribute will affect the target output. Commonly we have observed that blood pressure and diabetes are the major reason for cardio vascular diseases. This paper presents some technologies and methods for Machine learning. It then highlights some of the concerns about cardiovascular disease and how attributes are related to the output and how machine learning and data mining techniques are used to predict cardiovascular diseases, using the output of given dataset we can save life. The data is analyzed and results are observed. This section has a review of machine learning algorithms such as the Support Vector Machine, Decision Tree, ANN, Hidden Naïve Bayes, Naïve Bayes, Random Forest, K-Nearest Neighbors (KNN), and Particle Swarm optimization (PSO). The algorithms are applied to the datasets and on the basis of features or attributes of the dataset. All of these algorithms are used so, that the analysis is done in right direction.