Predictive Analysis of Cardiovascular Diseases

This paper is a short summary on the results of research on Heart Disease prediction from a data analytics point of view. Application of Machine learning algorithms on data analyzed from a patient provides reliable performance as that achieved by diagnosing Heart disease. The growth in technology has improved the information or data which can be extracted from a patient to help pinpoint the cause of illness. Using fourteen of such attributes or data from the medical profile of a patient can predict the chance of a patient developing a heart condition. In simple terms, these attributes are loaded into logistic regression, Decision Tree, Random Forest, SVM, KNN and Naive bayes, that is, Machine learning (ML) algorithms for the analysis and further prediction of heart disease. There are many other techniques, methods used by other researchers, however we have stuck to data analytics and the three algorithms mentioned earlier. By using this method, the standards in the medical industries are elevated and rose as they can provide better diagnostics and treatment of the patient, resulting in providing an overall good quality service. This paper has its main focus towards: Using Data analysis, creating prediction Models to provide early detection of Heart Diseases, Also by creating a reliable/cost efficient method to predict heart disease